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Progress in NeurobiologyVol. 29, pp. 1 to 55, 1987 Printed in Great Britain. All rights reserved 0301-0082/87/$0.00 + 0.50 Copyright © 1987 Pergamon Journals Ltd PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX: RAPID INDUCTION BY LEARNING NORMAN M. WEINBERGER and DAVID M. DIAMOND* Centerfor the Neurobiology of Learning and Memory, and Department of Psychobiology, University of California, Irvine, &vine, CA 92717, U.S.A. (Received 2 April 1986) Contents Abbreviations I. Introduction 2. Perspectives on neuroplasticity 3. The study of learning: strategies and caveats 3.1. Background 3.2. Learning and physiological plasticity 3.3. Pupillary conditioning 4. Organization of the thalamocortical auditory system 4.1. Prelude 4.2. Higher order auditory pathways 4.2.1. Background 4.2.2. The lemniscal line 4.2.3. The lemniscal adjunct pathway 4.2.4. The diffuse pathway 4.2.5. Resume 4.3. Approaching physiological plasticity in the auditory system 5. Physiological plasticity in the medial geniculate nucleus 5.1. Effects of learning 5.1.1. Compartmentalization of learning-induced plasticity 5.1.2. Plasticity at the level of single neurons in the magnocellular medial geniculate nucleus 5.2. Long term potentiation in the magnocellular medial geniculate nucleus 6. Physiological plasticity in auditory cortex 6.1. Background 6.2. Primary auditory cortex (A1) 6.2.1. Frequency specific habituation in primary auditory cortex 6.2.2. Associatively-induced plasticity in primary auditory cortex 6.2.2.1. Multiple unit activity 6.2.2.2. Physiological plasticity in primary auditory cortex at the level of single neurons 6.3. Secondary and ventral ectosylvian auditory cortex (AI1/VE) 6.3.1. Associatively-induced plasticity in secondary and ventral ectosylvian auditory cortical fields 6.3.2. Learning effects in different cortical laminae? 6,4. Associatively-induced plasticity of background activity 6.5. A note on auditory cortical plasticity and behavioral responses 6,6. Specificity of learning-induced changes in evoked activity 6.6.1. Background 6.6.2. Learning-induced changes in frequency receptive fields 7. Implications of learning-induced plasticity in auditory cortex 7,1. Introduction 7.2. Implications of physiological plasticity for conceptions of sensory cortical function 7.3. Adaptive functions 7.3.1. Introduction 7.3.2. Stimulus analysis 7.3.3. Response functions 7.3.4. Cognitive functions 7.3.4. I. Perception 7.3.4.2. Selective attention 8. Conclusions 8.1. Summary of findings 8.2. Relationships among physiological plasticity, neocortical function, learning, and sensory information processing 8.2.1. Physiological plasticity and learning 8.2.2. Auditory cortex, plasticity and learning 2 2 3 4 4 5 6 10 I0 11 11 12 13 13 14 15 15 15 15 17 18 21 21 23 23 27 27 28 29 29 31 33 34 34 34 35 38 38 39 40 40 40 41 41 41 42 43 43 44 44 44 * Current Address: University of Colorado Health Sciences Center, Dept. of Pharmacology, Box C-236, 4200 E. Ninth Avenue, Denver, Colorado, 80262. JPN 291 A 1 2 N.M. WEINBERGERand D. M. DIAMOND 8.3. Future directions 8.3.1. Introduction 8.3.2. Future cellular studies of physiological plasticity 8.3.3. Generality of sensory cortical plasticity 8.3.4. The basic paradigm of neurobiology Acknowledgements References 45 45 45 46 47 47 47 Abbreviations Behavior CS CS+ CSCR UR US Conditioned Stimulus Reinforced Conditioned Stimulus (Discrimination Training) Unreinforced Conditioned Stimulus (Discrimination Training) Conditioned Response Unconditioned Response Unconditioned Stimulus Brain Structures AAF AI AII D ICc ICp ICx MGN MGdc/vl MGm(M) MGv NLL PAF SAG VCN VE VL VO VPAF Anterior Auditory Cortical Field Primary Auditory Cortical Field Secondary Auditory Cortical Field Dorsal Division of the MGN Central Nucleus of the Inferior Colliculus Pericentral Nucleus of the Inferior Colliculus External Nucleus of the Inferior Colliculus Medial Geniculate Nucleus Dorsocaudal and Ventrolateral Subdivisions of the MGN Magnocellular (or Medial) Subdivision of the MGN Ventral Subdivision of the MGN Nucleus of the Lateral Lemiscus Posterior Auditory Cortical Field Nucleus Sagulum Ventral Division of the Cochlear Nucleus Ventral Ectosylvian Auditory Cortical Field Pars Lateralis of the ventral MGN Pars Ovoidea of the ventral MGN Ventral Posterior Auditory Cortical Field 1. Introduction F o u r fields in n e u r o b i o l o g y which are o f intense c u r r e n t interest are neuroplasticity, neocortical function, the n e u r o p h y s i o l o g y o f learning, a n d i n f o r m a t i o n processing in sensory systems. This c h a p t e r concerns the i n t e g r a t i o n o f these areas in the investigation o f learning a n d p h y s i o l o g i c a l plasticity in the a u d i t o r y cortex. T h e goals o f this c h a p t e r are threefold: (1) to p r o v i d e a s u m m a r y o f research which s u p p o r t s a highly d y n a m i c role for sensory n e o c o r t e x in learning, (2) to call a t t e n t i o n to the close relationships a m o n g the n e u r o b i o l o g y o f plasticity, cerebral cortex, learning, a n d sensory processing, a n d (3) to indicate research p a t h s that m a y p r o m o t e their integration. In the following sections, we discuss p h y s i o l o g i c a l plasticity as it relates to learning, followed by a brief overview o f the study o f learning as a p p l i e d to c o n d i t i o n i n g studies in animals. W e p o i n t out the pervasive a n d c o n t i n u a l c h a r a c t e r o f learning, e m p h a s i z i n g the r a p i d i t y at which a s s o c i a t i o n s between stimuli are formed, in c o n t r a s t to the slower rate at which specific m o t o r responses are acquired. F o l l o w i n g a s u m m a r y o f r a p i d learning, as indexed by p u p i l l a r y dilation, findings on p h y s i o l o g i c a l plasticity in the a u d i t o r y t h a l a m u s a n d especially in a u d i t o r y cortical fields are reviewed. W e focus on the specificity o f plasticity in cortical processing o f acoustic i n f o r m a t i o n d u r i n g learning. Next, i m p l i c a t i o n s o f n e u r o p l a s t i c i t y in a u d i t o r y cortex for c o n c e p t i o n s o f cortical o r g a n i z a t i o n a n d a d a p t i v e b e h a v i o r a l functions are considered. W e c o n c l u d e with suggestions a b o u t future directions for research in physiological plasticity in neocortex.* * For expository purposes, we use the following terms interchangeably: physiological plasticity, plasticity, and neuroplasticity. In this chapter, we do not cover morphological plasticity, including that which is related to learning (e.g. Greenough, 1984). PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 3 The findings presented herein are taken primarily from our own investigations. Despite their somewhat novel features, prior acquaintance with the subject matter is not required. The treatment of both conceptual and empirical issues assumes only a general knowledge of neurobiology. For several topics, the paper may serve as an introduction to the study of physiological plasticity and learning. Experts in the relevant areas may find unusual and possibly controversial material in the chapter. Selected aspects of some issues have been reviewed elsewhere (Weinberger, 1980, 1982a,b, 1984; Weinberger et al., 1984; Weinberger and Diamond, in press), and wherever possible, are referenced in the text rather than repeated here. 2. Perspectives on Neuroplasticity Neuroplasticity has become a major focus in contemporary neurobiology. It is widely studied at several levels--subcellular, cellular, neuronal systems, behavioral--and from various viewpoints--anatomical, biochemical and physiological. The topics most closely associated with neuroplasticity are (a) neural development, (b) recovery of function following pathology, (c) functional reorganization following sensory deprivation or peripheral manipulations, and (d) learning and memory. The first three examples have often been combined. For example, sensory deprivation is routinely used to study plastic characteristics of the developing visual system. Since the term "plasticity" has been introduced into neurobiology at several times, with various topics, its use has been varied. Therefore, it will be helpful to have a working definition at the outset. All definitions have in common the idea of change. For example, as recently defined, plasticity is " . . . a n y persistent change in the functional properties of single neurons or neuronal aggregates..." (Tsukahara, 1981). A slightly different definition put forth by Konorski (1967), is that "plasticity" is considered as a capacity or property of neural tissue, rather than as change, per se. This offers the advantage of conceptualizing neural substrates that are capable of expressing plasticity (under certain conditions), even prior to the actual change. Viewed this way, "plasticity" is one of two fundamental properties of nervous tissue; the other is "reactivity". The latter may be defined as the " . . . c a p a c i t y to be activated by stimulation, based on properties of excitability, conductivity and transmittability". "Plasticity" is then seen as the capacity for nervous tissue t o " . . , change its reactive properties as the result of successive activations." (Konorski, 1967, p. 7). For purposes of this chapter, we have adopted this "capacity" type of definition. It is also important to distinguish between non-associative and associative forms of plasticity. Habituation, defined as a decrement in response with repeated stimulation, is an example of the former. It is non-associative because it entails changes in reactivity as a result of repeating a single stimulus. By contrast, classical conditioning is an example of associative learning because one stimulus is explicitly paired with a second stimulus (see also Section 3.2).* The expression of neuroplasticity during development, following injury, and during learning has been documented extensively (for reviews see e.g. Lund, 1978; Finger and Stein, 1982; Tsukahara, 1981, respectively). Not generally recognized is the difference in time scales involved in these phenomena. Both developmental plasticity and that involved in recovery of function or reorganization may require months to be fully expressed, even years in the case of some phenomena in humans (e.g. language development). In contrast, neuroplasticity expressed during learning develops rapidly. Such changes are measured on the order of minutes and are often expressed after only one training experience. Furthermore, even when learning appears to be slow, one can discern behavioral signs of rapid acquisition of information (Section 3.2). This rapidity with which organisms acquire and store information indicates that underlying neuroplasticity must develop rapidly during learning. * For an introduction to associative and non-associative learning, see Domjan and Burkhard, 1982. 4 N.M. WEINBERGERand D. M. DIAMOND A second difference between developmental neuroplasticity and recovery of function, on the one hand, and learning on the other hand, involves their periods of occurrence. Developmental processes are generally limited to early stages of ontogeny, while recovery of function is limited to the period following insult to the nervous system. Furthermore, recovery of function is a response to a singular traumatic event, which rarely occurs repeatedly during the lifetime of an animal. In contrast, learning is a process in which an animal is in a continual state of acquiring information from its environment, including information about the consequences of its own behavior. It is, in fact, extremely difficult to prevent an animal from learning. Although learning can be prevented by the use of general anesthesia, under certain circumstances it can still occur even in this depressed state (Weinberger et al., 1984b). Informal validation of the pervasive and continual character of learning can be obtained by recalling and listing details of one's own experiences during the past 24 hr. While we have pointed out differences between learning and development/recovery of function, there are also important commonalities. For example, all are essential to behavioral adaptation. In addition, it is quite possible that the biophysical substrates of plasticity share common mechanisms. The capacity of nervous tissue to change its reactivity may be a highly conserved characteristic. As mechanisms of neuroplasticity in development, recovery of function and learning become understood, this issue will be resolved. 3. The Study of Learning: Strategies and Caveats Given that learning involves the continual acquisition of information, how has the content of a learning experience been addressed empirically, particularly in non-verbal organisms? In the following section we point out that the study of the acquisition of certain somato-motor responses has dominated both experimental psychology during the first half of this century and much of current neurophysiology of learning. 3.1. BACKGROUND Most contemporary studies of the neurophysiology of learning stress the need to develop a "model system" approach. The basic notion is to utilize a preparation that allows for identification of the circuitry underlying learning. In this context, "learning" is taken as the elaboration of a specific motor response, and the model system encompasses a circuit analysis of the pathway from the sensory receptors to the motor neurons (e.g. Woody, 1982). This approach is similar to the well documented stimulus response (S-R) approach which dominated both theory and experiments in the scientific study of learning in the era 1913-1955. S-R psychology was founded on the well-established reflex of Sherrington (1906) and others, and the conditioned reflex of Pavlov (1927). A complete account of attempts to understand learning exclusively in S-R terms, i.e. as the learning of only behavioral motor responses, cannot be given here. But the attempt was largely abandoned within experimental psychology by the end of the 1950s. Why was this so? Hindsight, as always, is perceptive. The experimental psychology of the late 19th and early 20th centuries took as its subject matter that which has always been of major interest, the contents of human consciousness. As its method, it adopted introspection, i.e. the reporting of one's own thoughts and experiences. Difficulty in cross-observer replication was inevitable with such a subjective method and attempts to study the contents of mind fell into disfavor (Boring, 1957). Leading a genuine scientific revolution, John Watson declared that the subject matter of psychology henceforth would be behavior, not the mind, and the method would be the observation of behavior (Watson, 1913, 1919).* Although some scientists used behavior * For an interesting account of the origins and early developmentsof behaviorism, see O'Donnell. 1985. PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 5 to infer mental or cerebral events, the dominant theme was to regard behavior as an end in itself. Consequently, the study of learning became largely a study of the modification of reflexes with experience. Unfortunately, while experimental psychology amassed valuable data and developed important experimental tools, it forgot that S-R behaviorism was merely a means of studying that which could be observed easily, i.e. the contractions of striated muscles which result in overt movements. Furthermore, many findings could not be explained by arguing that learning consisted only of the acquisition of responses. Undeniable evidence accumulated that much learning consists of learning relationships between events, such as between two stimuli, i.e. S-S learning. Gradually, the field of experimental psychology accepted that a behavioral response was not the only product of learning (for reviews see Dickinson, 1980 and Mackintosh, 1983; see also Mackintosh, 1985 and Rescorla, 1985). The scientific study of animal as well as human behavior is currently "cognitively" oriented. That is, the learning of information or knowledge is accepted and inferences about the involved processes and organization of information are made on the basis of behavior.* 3.2. LEARNING AND PHYSIOLOGICALPLASTICITY Given that learning involves not merely the acquisition of responses, but also the acquisition of information about relationships between stimuli, it is still necessary to have a behavioral index of learning. A behavioral response that develops due to the association of two stimuli permits the inference that neural processes underlying learning have developed plasticity. Most contemporary studies of physiological plasticity during learning employ a type of associative training first discovered and elucidated by Pavlov (1927), called "classical conditioning". In this form of training, a neutral stimulus (the conditioned stimulus, CS) is paired with a biologically significant event, such as food or a noxious stimulus (the unconditioned stimulus, US). Presentation of the CS, or US, or both is called a trial. After several trials of CS-US pairing (the former always preceding the latter), the CS develops the ability to elicit an acquired response which is usually similar to the response previously elicited by the US (Mackintosh, 1974). This learned, anticipatory response is referred to as the conditioned response (CR). Development and performance of the CR is the operational sign that learning has occurred. A standard example is the use of an acoustic stimulus as the CS, air puff to the eye as the US, and the development of an eyeblink as the CR. The "model systems" approach discussed above concentrates on attempting to trace the involved circuitry from the CS to the CR. In the example given above, this would involve circuitry from the auditory system to mechanisms that produce the conditioned eyeblink; circuitry for the unconditioned response (UR), that is, the eyeblink directly caused by air puff to the eye, would be different to some degree because the CR is learned while the UR occurs prior to learning. While this approach is productive, it fails to account for the fact that "the C R " is actually only one of several conditioned responses that develop during associative conditioning (Konorski, 1967; Weinberger, 1982a; Mackintosh, 1985). Thus, rapidly acquired CRs are evident for many responses, including heart rate, blood pressure, respiration, general body movement, galvanic skin response, and pupillary dilation. In fact, these responses are acquired more rapidly than is the eyeblink or leg flexion CR (Table 1). A further difference is that the rapidly acquired CRs develop more or less simultaneously within an animal in classical defensive conditioning situations, regardless of the particular US employed. In contrast, only one slowly developing CR is obtained, and it is specific to the US used. Thus, eyeblink CRs develop when air puff is applied as the US, and flexion CRs are seen only when shock is delivered to a limb, but cardiac conditioned * Strict S-R behaviorismcontinuesin a narrower arena, mainlyfollowingthe formulationsof B. F. Skinner. For a literate defenseof behaviorism,see Skinner, 1974. A recentmore technicalanalysis,which favors a greatly liberalizedbehaviorism,has been providedby Zuriff(1985). Argumentsagainst S-R approachesand for cognitive interpretations of animal behavior are given in Hulse et al., 1978. 6 N . M . WEINBERGERand D. M. DIAMOND responses develop in both cases. The slowly acquired CRs may be emphasized because they can be observed more easily, but the rapidly acquired CRs are earlier and more sensitive indicators of learning. The distinction between rapidly and slowly acquired CRs has at least three implications for the study of physiological plasticity during classical conditioning: First, associative conditioning appears to be a two stage process. In the initial stage, the organism learns that the CS predicts the US, as evidenced by rapidly acquired CRs that are not tied to the particular US. In the second stage, it then learns to make the somatic response that is specific to the unconditioned stimulus (Weinberger, 1982a).* Second, this two-stage process implies two alternatives about the physiological plasticity underlying behavioral plasticity: (i) the rapid development of CRs might involve widespread plasticity in diverse neural systems, followed by additional modifications in the more restricted circuitry underlying the development of specific somatic CRs; or (ii) the rapid changes in neural activity may be restricted to certain systems (e.g. sensory systems processing the CS and the US), with minimal involvement of circuitry underlying the specific somatic CR, In the latter case, changes in the circuitry of the somatic CR would develop, d e not,o, very late in the training procedure. If this turns out to be the case, then analyses limited to neural processes underlying slowly developing CRs may yield the mistaken conclusion that behavioral and neural plasticity develop only after prolonged training. Third, because rapidly developing physiological plasticity precedes the appearance of slowly acquired CRs, it could contribute to neural changes which underly these specific somatic conditioned responses. One way of taking account of the rapid/slow CR dichotomy is to attempt to determine the neural mechanisms involved in elicitation of a rapidly developing conditioned response, such as heart rate (e.g. Cohen, 1985). While response-oriented studies are valuable, they do have limitations. For example, since many rapidly acquired CRs develop more or less simultaneously, physiological plasticity involved in the production of a CR in one effector system is not necessarily of greater importance than that which develops in another effector system. Furthermore, there is no compelling rationale to focus on response systems to the exclusion of other neural systems, because even rapidly acquired conditioned responses are still simply behavioral indicators of underlying associatively-induced physiological plasticity (see also Olds e t al., 1972). Rather, any and all neural systems that rapidly develop physiological plasticity are excellent candidates for investigation of initial events in learning. The strategy that we employ is to analyze processing of the conditioned stimulus within its sensory system during learning; in our case, this is the auditory system. Recording in auditory pathways during learning has an empirical basis, as there is a substantial literature describing learning effects in this system (Weinberger e t al., 1984a), particularly in the auditory cortex (Section 6.1). There are also conceptual reasons for this line of inquiry. For example, since initial events in associative learning concern relationships between the CS and the US, plasticity might develop in the sensory systems which process these stimuli; this could be particularly true for the conditioned stimulus because its signal value is changed by conditioning. Also, unlike circuitry involved in specific motor responses, plasticity in the sensory system of the CS need not be linked to any particular effector action. Therefore, it may be involved in general processes of associative learning. 3.3. PUPILLARY CONDITIONING Our studies of physiological plasticity in the auditory system, while not concerned with the circuitry of any specific learned motor act, nonetheless require a behavioral conditioned * For a reviewof various two-processtheories of learning, see Rescorla and Solomon, 1967. Konorski (1967) is responsiblefor the first explicitdistinction between two types of classicallyconditioned responseswhose rates of acquisition differ. Recently,Thompson et al. (1984)have offereda three-factor hypothesisconsistentwith the two-process view of Weinberger, 1982. PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX TABLE 1. RATES OF DEVELOPMENT OF VARIOUS CONDITIONED RESPONSES Conditioned Response Rate (Trials)a Subject Referencesb,c Rapidly Developing Conditioned Responses Galavanic Skin Response Pupillary Dilation Blood Pressure Non-Specific Motor Respiration Heart 5-10 5-10 5-12 5-10 2 5 2-10 10-15 10 15 12 2-10 5-10 2-5 24-36 10 5-10 Rat Cat Cat Cat Cat Rabbit Rat Rat Rat Rabbit Lizard Fish Rat Rabbit Lizard Holdstock and Schwartzbaum, 1965 van Twyver and King, 1969 Gerall and Obrist, 1962 Ashe et al., 1976, 1978 Diamond and Weinberger, 1984 Yehle et al., 1967 deToledo and Black, 1966 Parrish, 1967 Rescorla, 1968 Yehle et al., 1967 Davidson and Richardson, 1970 Woodward, 1971 deToledo and Black, 1966 Yehle et al., 1967 Davidson and Richardson, 1970 Slowly Developing Conditioned Responses Nictitating Membrane Eyelid (Airpuff) Flexion, Leg 70-164 56-64 64-112 160-240 125 300 Flexion, Elbow Eyelid (Glabella Tap) 600 600-900 Rabbit Rabbit Rabbit Rabbit Cat Cat Cat Cat Gormezano et al., 1962 Berger and Thompson, 1978 Berry and Thompson, 1979 Schneiderman et al., 1962 Bruner, 1969 O'Brien and Packham, 1973 Tsukahara et al., 1981d Woody and Brozek, 1969e a Earliest consistent CR's. In cases where the authors did not specify the rate of learning, estimates were obtained from the published data or author's comments. b Citations are representative and do not constitute an exhaustive list. c Rapid and slow CR's have been recorded within the same animal. See, e.g. Dykman et al., 1965; Yehle, 1968. See also Black, 1965 and Schneiderman, 1972. a Stimuli employed were electrical stimulation of the brain. eThe rate of conditioning can be facilitated by stimulation of the hypothalamus (Kim et al., 1983). response to p r o v i d e i n d e p e n d e n t evidence o f learning. A s shown p r e v i o u s l y in T a b l e 1, several c o n d i t i o n e d responses are useful i n d i c a t o r s o f r a p i d associative learning. W e selected the p u p i l l a r y d i l a t i o n c o n d i t i o n e d response due to its reliability, r o b u s t n e s s a n d ease o f q u a n t i f i c a t i o n ( C a s s a d y e t al., 1982). F i g u r e 1 p r o v i d e s e x a m p l e s o f p u p i l l a r y d i l a t i o n responses to an a c o u s t i c stimulus d u r i n g three p h a s e s o f training:, " s e n s i t i z a t i o n " , " c o n d i t i o n i n g " a n d " e x t i n c t i o n " . D u r i n g c o n d i t i o n i n g , the CS a n d U S are paired; this is the s t a n d a r d s i t u a t i o n for associative c o n d i t i o n i n g . In c o n t r a s t , d u r i n g the sensitization phase, the CS a n d U S are n o t p a i r e d , b u t p r e s e n t e d m o r e o r less at r a n d o m intervals; h o w e v e r the p r o b a b i l i t y o f the CS a n d U S are the s a m e (e.g. 2/min) d u r i n g b o t h phases. D u r i n g extinction, the U S is o m i t t e d a n d the C S is p r e s e n t e d alone. T h e sensitization p h a s e is n e e d e d to c o n t r o l for n o n - a s s o c i a t i v e factors which m i g h t p r o d u c e a n a p p a r e n t c o n d i t i o n e d response d u r i n g pairing. F o r e x a m p l e , a n o r g a n i s m m a y simply give a larger response to a n y stimulus, such as a tone, due to the i n t r o d u c t i o n o f a s t r o n g stimulus, such as the u n c o n d i t i o n e d stimulus. This effect, t e r m e d " s e n s i t i z a t i o n " is n o t associative because it d o e s n o t d e p e n d u p o n the p a i r e d r e l a t i o n s h i p between the CS a n d the US. B o t h b e h a v i o r a l a n d n e u r o p h y s i o l o g i c a l responses to the CS s h o u l d be m e a s u r e d d u r i n g a sensitization a n d a c o n d i t i o n i n g phase.* Since the o n l y difference between the two t r a i n i n g phases is in the p a i r e d r e l a t i o n s h i p between the CS a n d US, a n y differences in n e u r o n a l a n d b e h a v i o r a l responses to the CS d u r i n g the c o n d i t i o n i n g p e r i o d can be a t t r i b u t e d specifically to the a s s o c i a t i o n o f the CS with the US. A c c o r d i n g l y , we * Other control procedures are possible, including having different groups of animals undergo sensitization and conditioning. Further, during sensitization, the CS and the US may be presented randomly or explicitly unpaired. Related technical details are not covered here; our purpose is to underscore the need for controlling non-associative factors during conditioning. 8 N.M. WEINBERGER a n d D. M. DIAMOND PUPILLARY BEHAVIOR ISENSITIZATIONI C5 ~ U5 A C 10 ~.~.......~. U 10 _ ~ [CONDITIONING~ CS 3 ~ CS 12 ~ . ~ CS 4 ~ CS 40 [EXTINCTIONI E4 ~A E 24 -,~-~--.-~ A FIG. I. Pupillary behavior during training. Sample records are for individual trials during one training session (dilation is up). Filled triangles indicate the onset of the CS (I sec duration), open triangles indicate the onset of the US (250 msec duration). The US was presented at the offset of the CS during conditioning. Whereas the tone alone evokes a low amplitude dilation during sensitization trials (C5, CI0), the US consistently evokes a large dilation. During conditioning, the evoked response to the CS is augmented by the fourth trial (CS4), and continues to increase in magnitude later in conditioning (e.g., CS12, CS40). During extinction, a decrease in the magnitude of the conditioned response is evident by the fourth trial (E4), and the behavioral response is virtually abolished by trial 24 (E24). routinely express responses to the CS during conditioning as differences from responses to this same stimulus during sensitization, The extinction procedure is useful to determine if, once established, conditioned responses still depend upon C S - U S pairing. The systematic reduction of CRs following removal of the US is evidence of this dependence. As shown in Fig. l, pupillary dilation is elicited by the CS during sensitization; these initial orienting responses decrease with repeated trials. In contrast, the response to the US (electrodermal stimulation, EDS) is larger and maintained throughout the training period. During conditioning (CS-US pairing) the pupillary CR is evident within a few trials. It increases in amplitude during continued pairing and is maintained throughout the conditioning phase. During subsequent extinction, when the CS is removed, the pupillary conditioned response decreases and finally disappears. Figure 2 presents group functions for sensitization and conditioning. Note the initial orienting response during sensitization which decrements, followed by the rapid development of the pupillary C R during conditioning. In fact, the dilation to the CS on trial 2 (the first trial following pairing) shows the initial conditioned response. That is, associative learning about the C S - U S relationship may develop as rapidly as possible, i.e. after one trial. In a series of studies over several years, we found that the pupillary C R exhibits all of the major characteristics of the more slowly acquired conditioned responses, except that it develops much more rapidly. For example, when the interval between the onset of the CS and the onset of the US is systematically increased (e.g. from l to 16 sec), the latency to peak dilation is also increased (Figs 3 and 4) (Oleson et al., 1973). This function relating C S - U S interval to CR latency is quantitative evidence that the conditioned stimulus PHYSIOLOGICAL P L A S T I C I T Y IN A U D I T O R Y CORTEX PUPILLARY BEHAVIOR 400 520 240 I,.l,J Z I i-Z 160 80 -80 -160 ~-- I I ,l,,I I 1 2 5 ] 2345 2 5 I I 4 5 6 I I I 7 8 9 SENSITIZATION 1~5 COND. CONDITIONING TRIALS BLOCKS OF FIVE TRIALS FIG. 2. Pupillary learning curves. Each point represents the mean ( + SE) of 10 subjects. Data points identified as 1-3 in sensitization and 1-9 in conditioning represent 5 trial averages. Values were computed as the percentage change in the tone evoked pupillary dilation from the average response for the last five trials o f sensitization. A significant increase in pupillary response is evident by the second b]ock of 5 trial averages, and it reached asymptote by the 20th trial. The rapid rate of acquisition is illustrated in finer detail for the first 5 trials of conditioning. 0 I I i I I I I I I j , i I I !-J , , t l _.1_ I ' ' • J J J~ | I I ,i+ '~ i i I I l I /L ,,lli,'''~_ .... ,B i tlJl I .... 2 I i t''' .l~', L . . . . . r..Ii',,, I .... il,+ I a~ J I I, FIG. 3. Pupillar dilation records for single trials at different C S - U S intervals from 2.5 sec. (A) to 16.0 sec. (F). Stimulus markers are solid lines; time marks are I sec. The largest response occurs in response to presentation of the US at CS offset. Note the conditioned response at the CS onset with a short interval (A), and the increasing latency of peak o f the CR as C S - U S interval increases; the peak of the CR occurs at the end o f the C S - U S interval in B-F. (From Oleson et al., 1973.) 10 N . M . WEINBERGER and D. M. DIAMOND E 16 14 12 Median Latency to Peak Response 10 oD 8 o/ 6 4 2 0 d5 ; CS-US 1"1h 1; ;'6 Inlerval FIG. 4. Median latency (sec) to peak dilation C R as a function of C S - U S interval, for each of five subjects (A E). Note the increase in latency to m a x i m u m response as CS US interval increases. (From Oleson et al., 1973.) becomes an acquired signal which animals use to predict the time of onset of the unconditioned stimulus. Additional experiments revealed that animals acquire discriminative pupillary conditioned responses (Ashe et al., 1978a; Ryugo and Weinberger, 1978). That is, the pupillary CR to a stimulus that is followed by a US (termed a C S + ) , is significantly larger than dilation to a stimulus that is not followed by a US (termed a C S - ) . This discrimination between a CS + and a C S - can be established both between modalities (e.g., acoustic and somatosensory, Weinberger et al., 1973) and also within the auditory modality (white noise vs pure tone, Oleson et al., 1972, 1975). Discrimination demonstrates that the association between the CS and the US is specific to the stimulus that signals the US. The pupillary CR also develops discrimination reversal. This occurs when the CS + (e.g., tone) and C S (e.g. white noise) are reversed such that the tone is no longer followed by the US, and the white noise is now paired with the US. During discrimination reversal, the pupillary conditioned response gradually shifts to the new C S + and declines to the new C S (Oleson et al., 1972; 1975). Discrimination reversal is a powerful demonstration that a discriminative conditioned response is not due to some unknown prior relationship between a CS and a US, but is attributable only to the stimulus pairing instituted by the experimenter. All of these findings show that the pupillary dilation conditioned response is a sensitive indicator that genuine associative conditioning develops rapidly. 4. Organization of the Thalamocortical Auditory System 4.1. PRELUDE Later in this paper, we summarize findings which document learning-induced plasticity in the thalamocortical auditory system. Of note, they reveal differential physiological plasticity in different components of the higher auditory system. In order to provide a structure-function framework within which these findings can be considered, it is necessary to first review the organization of the auditory forebrain. PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX II 4.2. HIGHER ORDER AUDITORY PATHWAYS 4.2.1. Background Early descriptions of thalamocortical organization described two basic systems: (1) a main projection line conveying direct lemniscal information to the primary sensory cortical receiving area, and (2) intrinsic nuclei of the thalamus and cortical associational areas which were believed to be independent of direct ascending sensory afferentation (Rose and Woolsey, 1949). Subsequent findings forced a revision of this view. For example, the "intrinsic" thalamic nuclei were found to receive ascending input (Jones, 1985; Macchi, 1983; Winer and Morest, 1983). Furthermore, rather than a single sensory projection line extending to one primary sensory cortical field, there are parallel thalamocortical sensory pathways which terminate throughout the neocortex (see I. Diamond, 1982; Oliver, 1982; and Merzenich and Kaas, 1980 for additional discussion of parallel processing). One of the more important advances in understanding parallel processing in sensory systems is attributed to Graybiel (1972, 1973) who provided a schema for identifying functional pathways in the auditory, visual and somatosensory systems, based partially on their accessibility to primary sensory neocortical areas. Graybiel maintained the wellestablished primary sensory input channels to the cortex, or lemniscal lines, as high fidelity, modality specific pathways which maintain a strict topographic representation of the periphery. In addition, she proposed a second class of pathways which ascend in parallel to the lemniscal lines; Graybiel viewed these as "accessory conduction routes that are capable of handling input dimensions not conveyed by the precise encoding systems of lemniscal lines" (p. 242, Graybiel, 1973). \ I I I I 500 ~rn FIG. 5. Camera lucida reconstruction of the medial geniculate nucleus through the middle of the MGN of the adult cat (coronal section, Golgi-Cox). Abbreviations: ventral division, VL, parlateralis and VO, pars ovoida; magnocellulardivision, M; dorsal division, D. (From Ryugo and Weinberger, 1978.) 12 N . M . WEINBERGER and D. M. DIAMOND Taking Graybiel's analysis as the starting point, we have proposed three, rather than two, parallel pathways in the higher auditory system (Diamond and Weinberger, in preparation). While maintaining the lemniscal line, we further subdivide Graybiel's "accessory" route into "lemniscal adjunct" and "diffuse" pathways. Each pathway is centered on one of the three major subdivisions of the medial geniculate nucleus (Fig. 5) as described by Morest (1964) with further refinement by Andersen et al. (1980). 4.2.2. The lemniscal line Our conception of auditory lemniscal line processing is in accord with Graybiel's classification, and also corresponds to subsequent descriptions of the "core" pathway of Winer et al. (1977) and I. Diamond (1979), and "central" pathway of Oliver (1982) and Oliver and Hall (1978a). These authors identify this pathway as composed of auditory brainstem nuclei (e.g. ventral cochler nucleus, superior olivary complex), central nucleus of the inferior colliculus, ventral division of the medial geniculate nucleus (MGv) and primary auditory cortex (AI). Each of these structures is organized topographically, i.e. contains an orderly representation of the cochlea. Because the cochlea itself has a systematic frequency organization along the basilar membrane, it projects a functional "tonotopic" organization upon the auditory lemniscal pathway (e.g. Serkov and Volkov, 1983). These tonotopic structures are populated predominantly by neurons that are narrowly "tuned", i.e. respond to a narrow range of frequencies (e.g. Aitkin and Webster, 1972; Goldstein et al., 1968; Imig and Morel, 1985a,b; Merzenich et al., 1975; Phillips and Irvine, 1981; for reviews see Aitkin, 1976; Brugge and Geisler, 1978: Clopton et al., 1974; Imig and Morel, 1983; Merzenich et al., 1979). Although the earlier studies restricted the termination of the leminiscal line solely to AI, recent studies indicate that the cortical projection zone of this pathway extends beyond AI. Physiological data have revealed that there are at least four tonotopically organized auditory cortical areas, in addition to the primary field. These are the anterior (AAF), posterior (PAF), ventral posterior (VPAF) and ventral ectosylvian (VE) auditory fields (Andersen et al., 1980; Diamond, 1985; Knight, 1977; Phillips and lrvine, 1982; Phillips and Orman, 1984; Reale and lmig, 1980). As these fields receive projections from the ventral medial geniculate nucleus (MGv)* (Andersen et al., 1980: hnig and Morel, 1983; Morel and Imig, 1983; Niimi and Matsuoka, 1979), they are all considered as the cortical termination zone of the auditory lemniscal line; a similar revision was suggested by Merzenich et al. (1979). By contrast, secondary auditory cortex (All) receives no input from the lemniscal line and contains only broadly tuned neurons with no clear frequency organizationt (Andersen et al., 1980: Imig and Morel, 1983; Rigby et al., 1967; Reale and Imig, 1980; Schreiner and Cynader, 1984; Watanabe, 1959). The location of"these auditory cortical fields is presented in Fig. 6. * Early studies reported narrow tuning and presumably a single tonotopic representation in the ventral division (MGv). However, recent studies indicate that this gross parcellation can be further subdivided. Presently, four subdivisions of the auditory thalamus are recognized as containing narrowly tuned cells and tonotopic organization: pars ovoidea and pars lateralis of the ventral division proper, the deep dorsal subdivision of the M G N and the lateral division of the posterior nucleus (Calford, 1983; Imig and Morel, 1984a,b). As their respective projections to auditory cortex are similar, we have retained the MGv distinction as representative of the region of the medial geniculate nucleus that contains narrowly tuned cells, tonotopic organization and projects to tonotopically arranged auditory cortical fields. t It is necessary to clarify the status of the secondary field (AID, because it has changed drastically in recent years. Originally, AII was believed to be a single field extending from the posterior ectosylvian sulcus to the anterior ectosylvian gyrus and that it was organized tonotopically. However, anterior AII was found to be a distinct tonotopic field (the anterior field, AAF), (Knight, 1977). Recent evidence (Niimi and Matsuoka, 1979; Reale and Imig, 1980) suggested that a tonotopic field exists in posterior AI1. Our recordings of unit activity in awake cats corroborate tentative descriptions by Reale and Imig (1980) of tonotopy in posterior AII. We refer to this posterior region as the ventral ectosylvian field (VE). AII as it is presently defined, is bordered by three tonotopic fields: AI, A A F and VE (Fig. 16). In this newly defined All region, there is no evidence of frequency organization or narrow tuning. PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 13 FIG. 6. Lateralviewof the auditorycorticalfieldsin the cat. Major sulci are indicatedwith thick lines and cortical field borders are in dashed lines. Dorsal is up, anterior is left. Abbreviations: AAF, Anterior AuditoryField; AII, SecondaryAuditoryCortex; AI, Primary AuditoryCortex; PAF, PosteriorAuditoryField;VPAF,Ventral PosteriorAuditoryField;VE, Ventral Ectosylvian Auditory Field. 4.2.3, The lemniscal adjunct pathway The original distinction between the leminiscal line and lemniscal adjunct pathways was based on auditory response properties and connectivity. These two pathways were seen as ascending in parallel from the midbrain to cortex. The lemniscal line presumably provides an accurate representation of the acoustic environment, while the lemniscal adjunct pathway is less tightly coupled to the physical components of sound. Although we have retained this "information processing" based distinction between auditory pathways, recent data indicate that finer distinctions can be made within the lemniscal adjunct pathway. For example, the magnocellular (MGm) and dorsocaudal/ventrolateral (MGdc/vl) subdivisions of the medial geniculate nucleus were both considered as components of the lemniscal adjunct pathway. However, their connectivity and physiological properties indicate that they process acoustic information differently. We briefly describe physiological and anatomical characteristics of MGdc/vl here, while properties of the MGm are discussed in the next section. Neurons in MGdc/vl are broadly tuned and respond to sound at long latencies, e.g. 30-160 msec. There is no evidence of tonotopic organization in either of these subnuclei of the MGN (Aitkin et al., 1981; Calford, 1983; Calford and Webster, 1981). The lack of narrow tuning and frequency organization in MGdc/vl is consonant with their afferents from lower auditory structures; midbrain inputs originate exclusively in non-lemniscal line structures which contain broadly tuned neurons, e.g. nucleus sagulum, external and pericentral nuclei of the inferior colliculus (Aitkin et al., 1981; Calford and Aitkin, 1983; Henkel, 1983). In contrast to the thalamic lemniscal line (MGv), which terminates solely in tonotopically organized cortical fields, thalamocortical projections of MGdc/vl terminate in both tonotopic (PAF, VPAF, VE) and non-tonotopic (AII) fields (Fitzpatrick et al., 1977; Imig and Morel, 1983; Morel and Imig, 1983). For present purposes, MGdc/vl and all structures directly connected with these subdivisions are considered to be the "lemniscai adjunct" pathway. 4.2.4. The diffuse pathway The magnocellular subdivision of the medial geniculate nucleus (MGm) is the only component of the auditory thalamus that receives convergent ascending input from midbrain components of both lemniscal line and lemniscal adjunct pathways. Thus, its afferents include projections from the central nucleus of the inferior colliculus (ICc) and nuclei of the lateral lemniscus, both of which are tonotopic, and from the external nucleus of the inferior colliculus (ICx) and the deep layers of the superior colliculus (SCd) which are not tonotopic (Aitkin et al., 1975, 1978, 1981; Graham, 1977; Henkel, 1983; Kudo and 14 N.M. WEINBERGERand D. M. DIAMOND CORTEX THALAMUS MIDBRAIN BRAINSTEM FIG. 7. Schematic diagram of the organization of higher order pathways in the auditory system, as described in the text. The lemniscal line is striped left, the diffuse pathway is solid and the lemniscal adjunct in striped right. According to this schema, cortical and midbrain regions may be components of more than one pathway, e.g. ICc, AAF and AI are connected with both, the thalamic subdivisions of the lemniscal line (MGv) and diffuse pathway (MGm); ICx and All are connected with the diffuse and lemniscal adjunct thalamic nuclei; and PAF, VPAF and VE are terminal fields of all three pathways. See Abbreviation Table for nomenclature. Niimi, 1980; LeDoux et al., 1985; Takada et al., 1985). Neurons in the M G m are responsive primarily to a broad range of frequencies and have no tonotopic organization (Aitkin, 1973; Calford 1983; Phillips and Irvine, 1979; Toros-Morel et al., 1981). That M G m neurons have the largest extent of dendritic arborization in the medial geniculate nucleus (Morest, 1964; Oliver, 1982; Winer, 1985; Winer and Morest, 1983), suggests that they may sample a wide range of afferentation. Therefore, the broad frequency tuning may result from a convergence of input from ICc cells which are narrowly tuned to different frequencies. Unlike the MGv, neurons in the M G m also respond to somatosensory stimuli (Khorevin, 1978; Khorevin, 1980; Love and Scott, 1969; Poggio and Mountcastle, 1960; Blum et al., 1979; Wepsic, 1966). The thalamocortical connectivity of M G m is unique in terms of its areal extent and cortical laminar relations. Unlike all other subdivisions of the M G N , which project to the middle layers in restricted portions of auditory cortex, M G m projects primarily to the most superficial layer, lamina I* (Jones and Burton, 1976; Niimi and Naito, 1974; Niimi et al., 1984; see also Mitani and Shimokouchi, 1985; Mitani et al., 1985). In addition, cortical projections to M G m originate in layer V, while descending afferents to other subdivisions originate in layer VI (Diamond, 1982; K a w a m u r a and Diamond, 1978; Kelly and Wong, 1981). Also unique, the M G m projects to every one of the several fields that comprise the auditory cortex (Andersen et al., 1980; Bentivoglio et al., 1983; Niimi and Matsuoka, 1979). Because of the widespread convergence of inputs and extensive efferentation, we designate M G m and all structures to which it is directly connected as the "diffuse" pathway. 4.2.5. R e s u m e The organization of higher auditory pathways is represented schematically in Fig. 7. The lemniscal line is striped left, the diffuse pathway is solid and the lemiscal adjunct pathway is striped right. In this schema, cortical and midbrain areas can be components of more than one pathway. For example, the central nucleus of the inferior colliculus (ICc), an * Projections of MGm to layer I throughout auditory cortex have been found in all species which have been studied (e.g. rhesus and squirrel monkeys, cat, rat and tree shrew). Certain species such as the tree shrew (Oliver, 1982; Oliver and Hall, 1978) and rat (LeDoux et al.. 1985; Ryugo and Killackey, 1974; Herkenham, 1980) also have MGm projections to the lower layers as well. PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 15 TABLE 2. COMPLEMENTARY NATURE OF EXPERMENTAL PARADIGMS OF SENSORY PHYSIOLOGY AND LEARNING Stimulus Parameters Sensory Physiology Learning Physical Psychological Vary Constant Constant Vary obligatory relay for most ascending auditory information, is tonotopically organized and projects a topographic representation of the cochlea upon the thalamic lemniscal line (MGv). In addition, ICc has efferents to the MGm, which has broadly tuned cells and no apparent frequency organization. In the cortex, the primary field (AI) receives input from the lemniscal line (MGv) and also the diffuse pathway (MGm). By contrast, the secondary cortical field is a component of the leminiscal adjunct pathway (MGdc and MGvl) and diffuse pathways (MGm). The posterior fields (PAF, VPAF and VE) are components of all three pathways. 4.3. APPROACHING PHYSIOLOGICAL PLASTICITY IN THE AUDITORY SYSTEM A distinction may be made between two types of stimulus parameters which can affect the responses of neurons: physical and "psychological" parameters. Physical parameters are well known; for acoustic stimuli, they include frequency, intensity, and stimulus duration. Psychological parameters refer to the significance and meaning of stimuli. Foundational data in sensory physiology are obtained by determining the relationships between physical parameters and neuronal responses at all levels of the investigated system. These studies generally utilize anesthetized animals in order to maintain a constant behavioral state; apparently, anesthesia also eliminates the analysis of stimulus significance by the nervous system. In contrast, cellular sensitivity to psychological parameters is determined by recording physiological responses to a stimulus while its significance is varied and its physical parameters are held constant. (Table 2, see also Weinberger and Diamond, in press). The tripartate organization of the thalamocortical auditory system is based on anatomical and sensory physiological criteria. In order to determine if this organizational schema has functional significance for physiological plasticity, we recorded the discharges of neurons in various thalamic and cortical regions while the significance of a sound was changed during classical conditioning; the physical parameters of this conditioned stimulus were held constant. The change in stimulus meaning was independently verified by development of the pupillary dilation conditioned response. To date, we have studied cellular activity in the thalamic components of the lemniscal line (MGv) and diffuse pathways (MGm), and in auditory cortex, we have studied cellular activity in the primary (AI), secondary (All) and ventral ectosylvian (VE) fields. Each cortical field is a component of the diffuse pathway, by virtue of its connection with MGm. In addition to being components of the diffuse pathway, AI receives lemniscal line input, All receives lemniscal adjunct input, and VE is innervated by both lemniscal line and lemniscal adjunct pathways. 5. Physiological Plasticity in the Medial Geniculate Nucleus 5.1. EFFECTS OF LEARNING 5. I. 1. Compartmentalization of learning-induced plasticity In an initial experiment, we recorded the discharges of unit "clusters" (so-called multiple-unit recordings)* from microelectrodes chronically-implanted in the MGN. * Multiple-unit or cluster recordings consist of the discharges of more than one neuron; in most cases, the number of neurons contributing to the record is not reported or known. Neither is it known whether the same neurons are contributing to the record over a period of time. This type of data is usually obtained with a low-impedanceelectrode (< 1 Mohm). PUPIL 2oo 180 l i~ 120 100 8o o= 6o ~o o 20 i I I I H 2o4 i ZO -60 1 2 3 BLOCKS 45 OF F I V E 6 TRIAL~g 7 8 J MOv 2O0 180 160 ~-~ 1 4 0 ~: 1 2 0 N '7' 1 0 0 ~ °°t .o! ao i 2o+ o / z-0 - 6 0 i L 1 2 3 BLOCKS 4 5 OF F I V E i --4 8 6 TRIALS MGm 180 .°°t 160 140 - 100 ! ~' 8o " ~ 60 T g 20 40 - --60 2 1 2 3 BLOCKS 4 5 OF F I V E 6 7 8 TRIALS FIG. 8. Group functions (n = 6) for pupillary dilation, and multiple unit discharges in the ventral (MGv) and magnocellular (MGm) medial geniculate nucleus during classical conditioning in response to an acoustic conditioned stimulus. All values are referenced to the average response during sensitization (not shown). Negative values at the beginning of conditioning for the pupil and the M G m reflect the habituation of these responses during the prior sensitization period. Note the rapid increase in response for the pupil and the M G m and the lack of response plasticity for the MGv. Each point is the mean _+ SE. 16 PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 17 Responses to the acoustic CS were recorded simultaneously from two subdivisions of this nucleus during pupillary conditioning (Ryugo and Weinberger, 1978). Behavioral associative learning was rapid, as evidenced by development of the pupillary dilation conditioned response to the conditioned stimulus in an average of 16.3 trials. Of greater importance, discharge plasticity developed as quickly in the medial geniculate nucleus. Most noteworthy, there was a clear difference in physiological plasticity within the M G N . Neurons in the lemniscal line (MGv) failed to exhibit any plasticity; that is, they responded to the acoustic CS without change during learning. In stark contrast, neurons in the diffuse subsystem (MGm) developed systematic and highly significant increases in response to the CS during conditioning; no such changes were found during the preceding sensitization control period (Fig. 8). Furthermore, during subsequent discrimination training between two acoustic stimuli (one paired with the US, the other unpaired), discriminative neuronal changes developed only in the magnocellular MGN.* This compartmentalization of learning in the M G N has also been found for other species and conditioning paradigms. Gabriel et al. (1975) obtained similar results in rabbits trained in an instrumental active avoidance task. Also, these findings have been reported for the rat during appetitive training utilizing a hybrid classical-instrumental conditioning task (Birt et al., 1979; Birt and Olds, 1981). Thus a functional distinction at the level of the thalamus between the lemniscal line (ventral M G N ) and diffuse pathway (magnocellular MGN), based on the differential capacity to develop discharge plasticity during learning, is maintained across species, and is expressed during both appetitive and defensive learning.# 5.1.2. Plasticity at the level o f single neurons in the magnocellular medial geniculate nucleus The results obtained with multiple-unit or "cluster" recordings provide an adequate entry point into the analysis of physiological plasticity. However, they may not provide a valid basis for inferences about the detailed involvement of single neurons in plasticity because each of the individual neurons may not be affected in the same way by associative processes. This could not be revealed by recordings in which the discharges of many neurons are combined. The problem would be resolved by computer-assisted sorting of unitary waveforms, but this technology is still in the developmental stage (Schmidt, 1984). Therefore, we recorded activity directly from one neuron throughout each training session. In a fine-grain analysis of physiological plasticity in the magnocellular M G N , we obtained single unit data during pupillary conditioning (Weinberger, 1982a). Most cells (71%) developed significant changes in response to the acoustic CS during conditioning. Surprisingly, the directionality of the changes was not consistent with that of the changes in multiple unit activity. Although there were increases in evoked activity (Fig. 9), 29% of the plastic neurons developed significant decreases during learning. A commonality between the multiple and single unit recordings was that changes developed rapidly (10-20 trials) (Fig. 10). These findings provided the first data on the physiological plasticity of single neurons in the auditory system during the acquisition of a behavioral conditioned response. In extending prior multiple unit data, they also revealed heterogeneity of physiological plasticity. We will return to this important point later (Section 6.2.2.2). * Neurons in the "'dorsal" divisionof the MGN were also non-plasticduring learning. The recordingsites were all within, or in the immediatevicinityof the deep dorsal division of the MGN, which has narrowly tuned cells arranged tonotopically(Calford, 1983).Therefore, recordings in both the dorsal and ventral subdivisions of the MGN are considered as located in the non-plastic, thalamic lemniscal line. t This does not imply that the responses of the MGv to the same acoustic stimulus are constant. They may vary with the state of arousal (Imig et al., 1972;Humphrey and Orman, 1977;Orman and Humphrey, 1981;see also Ryugo and Weinberger, 1976).However,even when the number of spikes elicited by a stimulus varies, there is maintained a constancy of the overall pattern of discharges (Imig and Weinberger, 1973). In any event, associative learning does not alter responses in this nucleus. JPN 29/1--B 18 N.M. WEINBERGERand D. M. DIAMOND UI7 SENS COND COND PUPIL I COND FIG. 9. Histograms and pupillary records for a single neuron in the magnocellular medial geniculate nucleus during sensitization (SENS) and subsequent CS-US pairing (COND). Each histogram is the sum of discharges of five consecutive trials; bar size = 50 msec. The histograms are complete data for one training session: 10 trials of sensitization and 50 trials of conditioning. Consecutive records start at the top of the left column and read downward, ending at the lower right. Presentation of the CS is indicated by the horizontal bar under each histogram (1 sec). Note the increased response to the CS during conditioning compared to responses during sensitization. This associative increase developed within the first 5 trials of CS-US pairing (compare the second SENS histogram with the first COND histogram) and is maintained or increased (except for the 8th block) throughout conditioning (compare the lower right histograms with the SENS histograms). Calibration, 12 spikes per division. Sample pupillary records (right) read from top to bottom: sensitization, trials 1, 7, 10; conditioning, trials 1, 5, 10, 20, 30, and 45. Note the decrement in pupillary response during sensitization and the development of the conditioned response during conditioning. Note also the constant size of the dilation to the US throughout conditioning, and the growth of the CR relative to this unconditioned response. Stimulus markers, CS = 1 sec, downward marker = US. (From Weinberger, 1982.) 5.2. LONG TERM POTENTIATION IN THE MAGNOCELLULAR MEDIAL GENICULATE NUCLEUS The p h y s i o l o g i c a l plasticity that develops r a p i d l y in the m a g n o c e l l u l a r M G N d u r i n g learning could be s e c o n d a r y to plasticity that develops elsewhere, o r it could d e v e l o p locally. O n e way o f a p p r o a c h i n g this issue is to d e t e r m i n e if s y n a p t i c plasticity can be i n d u c e d within this nucleus. A form o f s y n a p t i c plasticity that is o f p a r t i c u l a r relevance to learning was first described by Bliss a n d L o m o (1973), who f o u n d that brief, high frequency s t i m u l a t i o n o f the p e r f o r a n t p a t h resulted in a long lasting e n h a n c e m e n t o f s y n a p t i c t r a n s m i s s i o n in the h i p p o c a m p u s . Subsequently, this " l o n g term p o t e n t i a t i o n " ( L T P ) has been a n a l y z e d extensively (e.g. L y n c h et al., 1982). W e a p p l i e d this technique to d e t e r m i n e if s y n a p t i c plasticity c o u l d be induced in the M G m ( G e r r e n a n d W e i n b e r g e r , 1983). M o n o s y n a p t i c field potentials in the M G m were e v o k e d by single pulse s t i m u l a t i o n o f a m a j o r a s c e n d i n g input, the b r a c h i u m o f the inferior colliculus (BIC). H i g h frequency s t i m u l a t i o n i m m e d i a t e l y i n d u c e d LTP: the a m p l i t u d e o f the response increased a n d its PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEx GROUP N = A 17 .50 ,tO t14 L 30 20 10 O -10 -20 -30 -40 -50 1 2 BLOCKS 3 FOUR OF GROUP N = .4 TRIALS ,5 4 TRIALS 5 [] 10 50 .4.0 i 20 10 F. ~ --10 ~ --20 ~ -30 -40 -50 1 2 BLOCKS ~. FOUR OF GROUP N = C 7 5C ,,lc r-- ~ 2c N c ~.~ - - 1 0 N ~ --20 -30 -40 -50 1 2 BLOCKS OF 3 FOUR ~TRIALS 5 F I G . 10. Group functions for the discharges of single neurons in the magnoce]lular medial geniculate nucleus during conditioning. Values are the mean percent change (_+ SE) from the sensitization period (not shown). Data are grouped according to whether neurons developed an increase (A), no systematic change (B), or decrease (C) in response to the conditioned acoustic stimulus. Note the significant increases and decreases by the second block of conditioning (trials 5-8). 19 20 N . M . WEINBERGERand D. M. DIAMOND c A Itcolo itco,o i ORTHO l , ~ h iI i ,/ I • , Ii PRE ii !'/ ji' ANTI I : ;' !y,' PRE POST POST l>I J t It " 30' t 2' PRE HF 2' II 30' 60' POST HF I FIG. l l. Long-term potentiation in the magnocellular medial geniculate nucleus. A: Schematic representation of electrode placements in the magnocellular medial geniculate nucleus (MGm), brachium of the inferior collicnlus (BIC) and inferior colliculus (IC). B: Representative MGm responses to single stimuli delivered to the BIC at various times before (PRE) and after (POST) high frequency (HF) stimulation of the BIC. C: Representative MGm (ORTHOdromic) and IC (ANTIdromic) responses to BIC stimulation 5 min before (PRE) and 40 min after (POST) HF stimulation of the BIC. Arrows indicate time of stimulus; triangles indicate pints used for amplitude and latency measurements. Calibrations: l msec and 100 microvolts. (From Gerren and Weinberger, 1983.) latency decreased. Both signs of LTP were maintained for the duration of the recording session (60-180 min) (Figs 11 and 12). Potentiation of the evoked potential amplitude and reduction of its onset latency were also observed for input/output functions (Fig. 13). The enhancement of synaptic efficacy recorded in the magnocellular MGN was not due to a change in the excitability of the BIC inputs, because it developed in the absence of any change in the antidromic response within the inferior colliculus (Fig. 13). Long term potentiation in this nucleus also was obtained at the level of single units. Following brief high frequency stimulation of the BIC, the following changes developed immediately: (1) a decrease in spike latency; (2) an increase in the number of evoked discharges; (3) a decrease in the variability of latency of the first spike; and (4) a decrease in the variability of interspike discharges within an evoked burst (Weinberger, 1982) (Fig. 14). These findings demonstrate that synaptic facilitation can develop locally in the magnocellular MGN. They also indicate that both LTP and learning-induced physiological plasticity can develop rapidly within the thalamic component of the diffuse auditory pathway. This strengthens the argument for a role of LTP in associative learning, and underscores the critical position of the magnocellular MGN as a possible contributor to physiological plasticity in the auditory cortex. That all auditory cortical fields are innervated by the magnocellular M G N leads to the expectation that cortical cells may be influenced by the synaptic plasticity which appears to be generated within this nucleus during learning. In the following sections, we present data on physiological plasticity in the primary (AI), secondary (AII) and ventral ectosyvian (VE) auditory cortical fields during learning. PHYSIOLOGICALPLASTICITY1N AUDITORYCORTEX 21 N=7 130 ~ II I 120 Z ok) 100 N=8 ........ B_. :E O1.5~ t_J Z 1.51 .,J 1.4~ I -I0 I [ [ I ~ HF I i I i i I0 i I i [ i i i i i i 20 30 TIME (rain) i i i i i 40 i t t t I i I i 50 FIG. 12. The effects of high frequency stimulation (HF) on the MGm monsynaptic response to BIC stimulation. A: Amplitude with respect to the pre-HF mean (dashed line). Each point represents the mean (+SE) for the 7 of 10 experiments in which amplitude changed. B: Mean (+SE) latency-to-minimum (maximum negativity) for the 8 of l0 experiments in which latency was changed following high frequency stimulation. Note the immediate and maintained increase in amplitude and decrease in latency. (From Gerren and Weinberger, 1983.) 6. Physiological Plasticity in Auditory Cortex 6.1. BACKGROUND That learning alters physiological indices of sensory cortical function has been known since the dawn of electroencephalography. The finding was serendipitous. Durup and Fessard (1935), were studying the blocking of the alpha rhythm by light flash in the visual cortex of man. They noted that this response of the electroencephalogram actually occurred prior to the presentation of the flash. They sought and found the source of this phenomenon. Prior to the presentation of each flash, they activated a camera to film the oscilloscope record; the shutter made an audible click. The alpha blocking (conditioned response, CR) was induced by paired presentation of the click (conditioned stimulus, CS) followed by the flash (unconditioned stimulus, US). The circumstances of this discovery of electrophysiological classical conditioning indicated the readiness of the brain to develop plasticity and also inaugurated research on the role of sensory cortex in learning. Since that first, inadvertent demonstration of neurophysiological conditioning, systematic research has provided abundant additional evidence of learning-induced plasticity in the visual, somatosensory and auditory cortices; the auditory cortex has received most attention. (General reviews of early findings have been provided by John, 1961; Morrell, 1961; and Thompson et aL, 1972; more recent examples of learning-induced physiological plasticity in visual and somatosensory cortex are Morrell et al., 1983; and Voronin et al., 1975, respectively.) The extensive investigation of the effects of learning on neurophysiological responses in auditory cortex is summarized in Table 3. This information reveals that learning effects have been reported over the last thirty years, in several mammals, using various training regimens, recording different types of neurophysiological activity. The phenomenon appears to be widespread and highly replicable. 22 N.M. WEINBERGERand D. M. DIAMOND N=7 lOO N:2 .,--~2---~" 100 .~...~--~---~ // 80 ~ 80 ~ 6o 60 ~ 40 X 40 ._1. a, :E MONO 2O 20 .< c I I i t I I I l i i i E 2.1 Z ~ ~E 1.9 t ~ i i l i N=2 N=8 2.3 I L 100 x 80 PRE HF . . . . POST HF 60 2 t..) 1.7 z 40 -~ 1.5 20 B I 2 3 4 5 6 7 8 STIMULUS LEVEL (V÷lO) FIG. 13. The effects of high frequency stimulation on input-output curves (I/0 functions) obtained 20 minutes prior to the start of the main experimental series (see Fig. 12) and at least one hour following high frequency stimulation. A and B: Mean amplitude and latency functions, respectively, for the MGm monosynaptic response to BIC single stimuli as a function of stimulus intensity. C and D: Amplitude functions for two experiments in which orthodromic (C) and antidromic (D) responses were recorded simultaneously. Note the orthodromic effect in the absence of any change in the antidromic response. Solid circles, before HF and open circles, after HF stimulation. Each point is the mean of the N value (+ SE) presented. The stimulus level corresponds to current intensities of 0.1-0.8 mA. (From Gerren and Weinberger, 1983.) T a b l e 3 also p r o v i d e s i n f o r m a t i o n a b o u t the inclusion o f two essential controls: (1) a c o n t r o l for n o n - a s s o c i a t i v e factors a n d (2) assurance o f stimulus c o n s t a n c y at the cochlea. T h e f o r m e r has been discussed p r e v i o u s l y (Section 3.3). It need only be recalled here that in the absence o f an a d e q u a t e control, such as c o m p a r i n g c o n d i t i o n i n g effects with responses to the same acoustic stimulus d u r i n g a sensitization period, one c a n n o t justifiably c o n c l u d e t h a t the o b s e r v e d effects are due to associative factors.* C o n t r o l o f the acoustic stimulus at the cochlea is m o r e difficult to achieve, b u t no less i m p o r t a n t . Unless stimulus c o n s t a n c y is d e m o n s t r a b l e , it m a y be impossible to d e t e r m i n e w h e t h e r changes in n e u r o n a l responses are due to intrinsic m e c h a n i s m s o f learning o r to learning-related changes in effective stimulus intensity. V a r i a b l e s which can p r o d u c e a spurious o u t c o m e include p o s i t i o n a n d distance o f the h e a d with reference to the s o u n d source ( M a r s h et al., 1962; Starr, 1964), a n d s o u n d - s h a d o w i n g by the p i n n a e ( W i e n e r et al., 1966). Use o f b o t h c o n t r o l p r o c e d u r e s was n o t instituted routinely before the mid-1970s (Table 3). In o u r case, for the d a t a p r e s e n t e d in following sections, we used a sensitization c o n t r o l p e r i o d preceding CS--US pairing. C o n s t a n c y o f the c o n d i t i o n e d stimulus at the cochlea was c o n t r o l l e d by m a i n t a i n i n g a fixed relationship between the s p e a k e r a n d the t y m p a n i c * Discriminative responses in the auditory system which have an extremely short latency (e.g. 4 msec) cannot be explained by stimulus inconstancy at the periphery (Disterhoft and Stuart, 1976). Such findings, even in the absence of explicit or verified control of the acoustic stimulus, provide adequate evidence of plasticity due to central processes. PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 28 23 A. PRE HF: I min 21 14 7 l 28 O3 LLJ V i i i i B. POST HF:2 rain 21 fi-14 O3 281 C. POST HI 52 min 21 14 7 0 10 15 rnse~ FIG. 14. Long term potentiation in the MGm at the level of single neurons. Records are from one cell at various times. The inset shows superimposition of burst responses to five single stimuli applied to the brachium of the inferior colliculus one minute preceding and thirty two minutes following a single train of high frequencies stimulation of the BIC. Note the variability prior to HF stimulation (top) compared to the lack of variability and also the decrease in latency to the first spike following HF stimulation (bottom). Calibrations: I msec, 100 microvolts. Histograms (left) are the sum of responses to 25 single test stimuli before (A), shortly after (B) and 32 minutes (C) following HF stimulation. Following HF stimulation, notice the decrease in latency to the first spike, increase in the probability of response to each test stimulus, increase in the number of spikes per burst, decrease in the variability of interspike intervals, and continuing trend of these change (compare B and C). membrane. Verification of acoustic control was obtained by documenting a lack of change in the cochlear microphonic during conditioning (Ashe et al., 1976). 6.2. PRIMARY AUDITORY CORTEX (AI) 6.2.1. Frequency specific habituation in primary auditory cortex Although the major focus of this paper concerns the effects of associative processes on discharge activity in auditory cortex, it is noteworthy that a form of non-associative plasticity also develops in this field. We refer here to habituation, which is a systematic decrement in evoked responses to repeated acoustic stimulation. While such decrements are attributable to learning per se, rather than to refractory or fatigue processes (Thompson and Spencer, 1966), they are not associative (see also Section 2). However, a comprehensive account of physiological plasticity in the auditory cortex must include the effects of habituation as well as associative learning. It is not yet known whether these two forms of physiological plasticity are related, particularly at the biophysical level (Hawkins and Kandel, 1984; Mackintosh, 1985), so it is germane to determine if they both develop in the same neural structures. N. M. WEINBERGERand D. M. DIAMOND 24 TABLE 3. NEUROPHYSIOLOGYOF LEARNING IN AUDITORY CORTEX Authors Year Animal Record '~ Behavior Galambos et al. 1956 Cat EP Jouvet Beck et al. Gluck and Rowland Roitbak et al. Marsh et al. Moushegian et al. Galambos and Sheatz 1956 1958 1959 1960 1961 196t 1962 EP EEG EEG EP EP EP EP Gerken and Neff Buchwald et al. Hall and Mark Mark and Hall Popova c Halas et al. Popova Sommer-Smith and Moricutti Popova Romanovska Saunders Olds et al. Disterhoft and Olds Cassady et al. Majkowski and Sobieszek Oleson et al. Disterhoft and Stuart Dolbakyan Gasanov and Galashina Woody et al. Kitzes et aL Dumenko and Sachenko Dumenko and Sachenko Dolbakyan e Dumenko and Sachenko Gasanov and Galashina Olds et al. Pukhov and llyushenok Galashina and Bogdanov Galashina and Bogdanov Popova and Uvarov Kraus and Disterhoft Weinberger et al. Diamond and Weinberger Diamond and Weinberger 1963 1966 1967 1967 1969 1970 1970 1970 1971 1971 1971 1972 1972 1973 1975 1975 1976 1976 1976 1976 1978 1979a 1979b 1980a 1980 1980 1980 1980 1981 a 1981 b 1981 1982 1984 1984 1986 Cat Cat Cat Cat Cat Cat Cat Monkey Cat Cat Rat Rat Dog Cat Dog Cat Dog (?at (?at Rat Rat Dog ('at ('at Rat Dog Cats Cat Cat Cat Cat Dog Cat Cat Rat Cat Cat Cat Dog Rabbit Cat Cat Cat Pupil, "Emotional" None? Flexion None Head Mov. None None Blink & Head Mov. Flexion Flexion Freezing Freezing Flexion Flexion Flexion Head Mov. None'? Eyelid Mov. Head Mov. Head Mov. Flexion Press Pupil Head Mov. Flexion None? Eyeblink Pupil Respir. Respir. Flexion Respir. None Head Mov. None'? Mov. Mov. None? NM Pupil Pupil Pupil MU MU EP EP EP MU EP EP EP EP EP MU MU MU EP MU MU EP S" S S S S EP S Sd MU EP S~ S'~ EP MU S S S Control b Acous. Non-Assoc. (Y) N N? N? N N N (Y) N N? N? Y N N N N N Y (Y) (Y) N? N? N N N? N (Y) (Y) (Y) N N N N N N Y N N N N N Y Y Y N Y N Y/? Y Y N N (Y) Y Y? Y? N Y? N N Y N N N? N Y Y Y ? N Y N Y? Y? N Y? N? Y N N N N Y Y Y Y a Denotes type of neurophysiological recording. Abbreviations: EP=evoked potentials; EEG=electroencephalogram; MU = multiple unit discharges; S = single unit spikes. b Indicates the presence or absence of controls for acoustic constancy ("Acous.") and non-associative factors ("Non-Assoc"). Abbreviations: Y = Yes; N = No; ( ) - P a r t i a l control; ? ~ Uncertain. c Translation of Popova, 1968. d Report of several single units separated only by amplitude discrimination, which is not widely accepted as adequate for demonstration of single unit discharges. Translation of Dolbakyan, 1980. H a b i t u a t i o n w a s i n v e s t i g a t e d b y r e c o r d i n g t h e d i s c h a r g e s o f n e u r o n a l c l u s t e r s in p r i m a r y auditory cortex (AI) during repeated presentation of tone or white noise bursts. Also, we r e c o r d e d s i m u l t a n e o u s l y t h e r e s p o n s e s o f n e u r o n s in t h e first c e n t r a l a u d i t o r y s t a t i o n , t h e v e n t r a l c o c h l e a r n u c l e u s , a n d p u p i l l a r y b e h a v i o r ( W e i n b e r g e r e t a l . , 1975). P u p i l l a r y dilation responses decremented systematically during repeated presentation of acoustic s t i m u l i (see a l s o C o o p e r e t a l . , 1978). C o n c u r r e n t l y , t h e r e s p o n s e s o f n e u r o n s in p r i m a r y a u d i t o r y c o r t e x a l s o h a b i t u a t e d . I n c o n t r a s t , t h e r e w a s n o effect in t h e c o c h l e a r n u c l e u s (see a l s o W e i n b e r g e r e t a l . , 1969). I n c r e a s i n g a r o u s a l , b y t h e a p p l i c a t i o n o f a n o v e l c u t a n e o u s s t i m u l u s , d i d n o t a l t e r h a b i t u a t i o n (Fig. 15). T h e s e f i n d i n g s (see a l s o e.g. S i m o n s e t a l . , 1966; a n d W i c k e l g r e n , 1968) i n d i c a t e t h a t n e u r o n s in t h e p r i m a r y a u d i t o r y c o r t e x a r e a f f e c t e d b y s t i m u l u s m e a n i n g d u r i n g n o n - 25 PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 1'50 140 150 i40 COCHLEAR N AUDITORY CX N:B PUPIL 120 120 I00 I00 I'-Z 8O W~ 8O ac,~ Q,.. 40 4O 20 2O 0 -20 ,, 5 I0 15 20 I 5 I0 15 20 I I , i 5 t, , ~ L I0 h , I t I~IL 15 i i I 20 -20 FIG. 15. Average responses of the pupil (n = 8), primary auditory cortex (n = 8), and lower auditory system (ventral cochlear nucleus) (n = 6) to repeated presentations of an acoustic stimulus (white noise burst, approximately 1/min). Open symbols denote background (pre-trial) levels; dosed symbols denote responses evoked by the acoustic stimulus. Note the decrement of evoked pupillary dilation and discharges in auditory cortex, in contrast to lack of change in the cochlear nucleus, during trials 1-15. Background levelsare unchanged during habituation. A novel stimulus (cutaneous stimulation, arrow) produces general arousal, as indicated by a large increase in background pupillary size, without any significant effecton habituation of evoked responses of the pupil and auditory cortex. The abscissa is trials (stimulus presentations) and the ordinate is the percent of response on the first trial for evoked (left ordinate) and background (right ordinate) measures. (From Weinberger et al., 1975.) associative learning (i.e. habituation). In this case, the stimulus meaning is that the sound is without signal value; this was assayed behaviorally by the systematic decrement in pupillary dilation to repeated acoustic stimulation. Habituation in AI is not due to peripheral or lower auditory system processes, in view of the lack of change in the cochlear nucleus. Furthermore, it is not attributable simply to a decrease in arousal because habituation is not altered by increasing arousal level. Although behavioral habituation to tones still occurs following removal of AI, more complex forms of habituation, such as habituation to an acoustic pattern, require an intact auditory cortex (Sharpless and Jasper, 1956). This indicates that discharge plasticity in AI during habituation may reflect higher order analyses of sounds than those occurring more peripherally. Habituation might reflect a general decrease in cortical responsivity or it might be specific to the repeated stimulus. To resolve the issue, we used two tones, only one of which was used explicitly as a habituating stimulus. (Westenberg et al., 1976; Westenberg and Weinberger, 1976). First, tones of two different frequencies were presented in an alternating sequence. Then one of the tones was presented repeatedly for 15 min. During this time, the evoked potential (EP) in AI systematically decreased in amplitude. Following this habituation o f the EP, the alternating tones were presented once again (Fig. 16, top). We compared responses to the alternating tones preceding and following repetition of the single tone. I f habituation were general, then responses to the non-repeated tone should be reduced as well as those to the repeated tone. This was not the case; the decrement was specific to the tone that was presented in isolation (Fig. 16, bottom). This design ruled out any possible confounding effects of change of behavioral state because the tones were presented in alternating sequence during the test periods (before and after the repeated single tone). In a reversal test, the EP habituation was specific to the second tone when it was the isolated, repeated stimulus (Fig. 16, bottom). This proved that habituation is specific to the stimulus that is repeated. These findings demonstrate that physiological plasticity develops within AI for a simple, non-associative form of learning, i.e. learning that a repeated stimulus has no important consequences. This was observed both for field potentials and for the discharges of neurons in response to acoustic stimulation. Furthermore, it is specific to the stimulus that is repeated, and therefore cannot be attributed to a general loss of responsiveness to acoustic stimuli or to a general change in the state of excitability of the auditory cortex. Finally, 26 N.M. WEINBERGER a n d D. M. DIAMOND the specificity of this phenomenon suggests that plasticity in AI is sensitive to specific informational aspects of acoustic stimuli. In the case of habituation, this appears to be concerned with rapidly learning that a potentially important stimulus actually has no particular significance. Repetit,ve-pre i , REPETITIVE Pip Jr Series RepehHve post H, Hz LO HZ Test-pre Test-post Test-pre Test - post Repetitive-ore --REPETITIVE A Pip S e r i e s - - Jr Repeht,ve- post Repetitive B Tes.__t f HI Repehhve H, Hz d " ,o ' e'o " 3o ~o ' 55 .~L. h 65 C ,5 a5 ' 93 " ,6o [6 ,o ~o LoHz 30 ~o ~0 " ~ 7o " 80 ~ ,oo ] 13 I .....7 \ LoHz ..... , ¸.¸,¸, I .... I II o " ,0 " 20 " 30 " 4b " 4o ' ~ " rb " e~ " 96 " ,oo ' 6 msec ,O " zO " /~ ' 4b " ~ " 6o ro a0 9o " , ~ I mse¢ FIG. 16. F r e q u e n c y specific h a b i t u a t i o n o f e v o k e d p o t e n t i a l s in the p r i m a r y a u d i t o r y cortex. T o p : d i a g r a m o f the e x p e r i m e n t a l p r o t o c o l . H i g h a n d l o w t o n e pips (100 msec) were first p r e s e n t e d a l t e r n a t e l y (l/sec) f o l l o w e d b y r e p e t i t i o n o f the h i g h t o n e (900 tones), a n d t h e n b y p r e s e n t a t i o n o f the initial a l t e r n a t i n g sequence; ( a d d i t i o n a l h i g h t o n e s p r e s e n t e d at 2 H z f o l l o w i n g this w e r e used f o r a n o t h e r c o n t r o l , n o t d i s c u s s e d here). A v e r a g e e v o k e d p o t e n t i a l s to the s a m e f r e q u e n c y delivered b e f o r e a n d a f t e r r e p e t i t i o n o f the h i g h t o n e a l o n e w e r e c o m p a r e d . T h i s p a r a d i g m w a s t h e n r e p e a t e d , c o u n t e r b a l a n c e d b y u s i n g the l o w t o n e as the r e p e a t e d s t i m u l u s (lower d i a g r a m ) . B o t t o m : a v e r a g e d e v o k e d p o t e n t i a l s to h i g h (3.2 k H z ) a n d l o w (1.0 k H z ) t o n e s b e f o r e ( " p r e " ) a n d f o l l o w i n g ( " p o s t " ) r e p e a t e d p r e s e n t a t i o n o f o n e t o n e alone. F r e q u e n c y - s p e c i f i c h a b i t u a t i o n is evident in the smaller c o m p o n e n t s o f " p o s t " e v o k e d p o t e n t i a l s d u r i n g the repetitive c o n d i t i o n . ( F r o m W e s t e n b e r g a n d W e i n b e r g e r , 1976.) PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX Auditory 27 Cortex Sens. Disc. Day 1 CS+ (WN) CS- (Tn) Day 8 CS+ (Tn) CS- (WN) FIG. 17. Discrimination and discrimination reversal in primary auditory cortex. Sample histograms of multiple unit activity in response to white noise (WN) and tone (Tn) during sensitization (acoustic stimuli and US presented randomly) and discrimination (one sound paired [CS + ], other sound unpaired [CS-] with the US). Each histogram is the sum of discharges on 10 consecutive trials. During the first training session, note the large increased response to the CS+ compared to changes in response to the C S - from sensitization to discrimination training. One week later (Day 8), training was repeated with the acoustic stimuli reversed. Note the neuronal "memory" of the original CS + (WN), revealed by the large response to WN at the beginning of Day 8 vs. response to this stimulus at the beginning of Day 1. Also note, the increased response to the CS + and decreased response to the C S - during training on Day 8. Horizontal line under each histogram is the stimulus marker (5 see). Ordinate, 96 spikes per division. 6.2.2. Associatively-induced plasticity in primary auditory cortex 6.2.2.1. Multiple unit activity H a b i t u a t i o n is p r o t o t y p i c a l o f n o n - a s s o c i a t i v e learning. In further experiments, we focused on associative effects at the a u d i t o r y cortex. In an early experiment, we r e c o r d e d m u l t i p l e - u n i t activity o f n e u r o n s in A I (Oleson et al., 1975). M i c r o e l e c t r o d e s were i m p l a n t e d c h r o n i c a l l y so t h a t recordings f r o m the s a m e cortical sites c o u l d be o b t a i n e d over days. C o n t r o l s for n o n - a s s o c i a t i v e factors (Section 3.3) included a sensitization p e r i o d , d i s c r i m i n a t i o n t r a i n i n g between two s o u n d s (tone a n d white noise), a n d s u b s e q u e n t d i s c r i m i n a t i o n reversal t r a i n i n g one week later. Behaviorally, the p u p i l l a r y C R d e v e l o p e d quickly, e x h i b i t e d d i s c r i m i n a t i o n between the C S + a n d C S - , a n d s h o w e d d i s c r i m i n a t i o n reversal when the relation between these stimuli a n d the U S was reversed. M o r e i m p o r t a n t l y , p h y s i o l o g i c a l plasticity also d e v e l o p e d r a p i d l y , in less t h a n 20 trials. E v o k e d activity to the C S + d u r i n g c o n d i t i o n i n g was significantly increased relative to sensitization. M o r e o v e r , there was a clear d i s c r i m i n a t i o n effect; the C S + e v o k e d significantly m o r e discharges t h a n the C S - . Signs o f long term s t o r a g e o f this p h y s i o l o g i c a l plasticity were also found: these a c q u i r e d differences were still present one week later. F i n a l l y , the d i s c r i m i n a t i o n effect reversed d u r i n g s u b s e q u e n t 28 N.M. WEINBERGERand D. M. DIAMOND reversal training, showing that the response plasticity was due to the associative relationship between the C S + and the US (Fig. 17). These results demonstrate that stimulus-specific discharge plasticity develops rapidly in primary auditory cortex during associative learning. Furthermore, the changes were not transient, but persisted until reversed by discrimination reversal training. 6.2.2.2. Physiological plasticity in primary auditory cortex at the level o f single neurons As pointed out above (Section 5.2), multiple-unit recordings have both advantages and limitations. Data from the magnocellular M G N revealed the insensitivity of such recordings to systematic decreased response to the conditioned stimulus. Multiple unit recordings in auditory cortex have also consistently reported increased evoked discharges to the conditioned stimulus (e.g. Disterhoft and Olds, 1972; Disterhoft and Stuart, 1976; Gabriel et al., 1982; Oleson et al., 1975). Considering the heterogeneity of plasticity in the magnocellular MGN, it seemed possible that cluster recordings in auditory cortex also could mask underlying heterogeneity of plasticity of the individual neurons. In order to address this issue, we recorded the discharges of one neuron in primary auditory cortex during each training session (Weinberger et al., 1984c). As with earlier studies, the pupillary CR developed rapidly (6-20 trials). Most neurons (63%) developed response plasticity to the CS during acquisition of the pupillary CR. Plasticity developed quickly; it was evident within five trials and attained statistical criterion in an average of 13.2 trials. As in the case of the magnocellular MGN, the directionality of the changes in evoked activity included increases and decreases. Changes in either direction were of equal likelihood. Group functions for changes in evoked activity are presented in Fig. 18. Apparently, as with the magnocellular MGN, significant decreases in evoked activity in auditory cortex were masked in the earlier multiple unit recordings. In order to evaluate this proposal, we constructed "multiple unit" histograms by compiling all data from each single unit record. Figure 19 presents a comparison of evoked activity of all units during two crucial parts of the training regimen: the last five trials of sensitization (which served EVOKED ACTIVITY, AI 250 F i , ,ooh t/ I-- 50 \ Y ~i : ]+, (~-iO0 \ ,,, -rodk rF I \ / ,-, % y / -200 \ i \T~ -250 o - - o I N C R E A S E n=6 o-. - o DECREASE n:7 ~----A NO CHANGE n=7 -300 -550 I I i, /\ 2OO ~ i L ~ ~3 I I / \,!/ l I {23~e SENSITIZATION ~ l ) i, i Bg,o CONDITIONING BLOCKS OF FIVE TRIALS FIG. 18. Group functions for changes in the evoked activity of single neurons in primary auditory cortex. The data were sorted according to whether the cells attained significant increases or decreasesin evokedactivityduring conditioning,or failure to developsuch changes ("no change"). Each point is the mean percentage change in evoked activity for blocks of five trials relative to the last five-trialblock during the sensitization period. Note that the developmentof increased and decreased evoked discharges is evident during the first block of conditioning (trials 1 5). Vertical bars denote _+SE. PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 29 AUDITORY CORTEX COMBINED "I)/I W E U~ mse¢ . . . . . . . . . . . 2.4 1 1.8 W ~tl 1.2 O'3 0.6 0 0 500 lO00 rnsec FIG. 19. "Pseudo-Multiple Unit" poststimulus histograms constructed by summing the number of discharges of all 21 neurons from which data were obtained during training. The numbers of spikes were determined for 500 msec immediatelypreceding presentation of the conditioned stimulus and for the 1000msec during presentation of the conditioned stimulus. Histograms depicted are for the last five trials of the sensitization period (SENS) and for a representative five trial block (trials 16-20) during conditioning (COND). Notice the increase in evoked response following onset of the conditioned stimulus during conditioning relative to sensitization. as the baseline measure for identifying plasticity during conditioning) and trials 16-20 during conditioning. These conditioning trials were selected because most discharge plasticity was present during this part of training. Note that there is an apparent increase in evoked discharges following the onset of the CS during trials 16-20 of conditioning, relative to the last 5 trials of sensitization. The decreases that were evident in single unit data are masked when the evoked activity of all neurons are combined into this pseudo-multiple unit activity record. Although this histogram record is not identical to standard multiple unit records because it was obtained from separate experimental sessions, various loci and its individual unit composition is known, it provides a picture of an increase in evoked activity with learning. Yet, the histograms of the individual neurons are heterogeneous, including decreases and no changes, as well as increases during conditioning. Thus, multiple unit records cannot be considered as representative of the activity of a homogeneous population of neurons which develop an increase in response. In addition, the single unit data indicate that it would be incorrect to conclude that the general excitability of auditory cortex simply increases during learning. 6.3. SECONDARY AND VENTRAL ECTOSYLVIAN AUDITORY CORTEX (AII/VE) 6.3. I. Associatively-induced plasticity in secondary and ventral ectosylvian auditory cortical fields Because VE is a recent discovery (see Section 4.2.2), it was not known as a cortical field distinct from A I I in previous published accounts. Therefore, in our first study of these 30 N . M . WEINBERGERand D. M. DIAMOND A. D-IC B. T'[-12A SENSITIZATION 2O TR 1-15 15 I0 II, , tUlt 5 I J CONDITIONING 20- ~4 61 ~l TR.l-15 "rD 1_1~ 15IO5- , ,I It t t 25-] . . . . . . TR 16-30 2015105- -lO00 -500 0 500 lOO0 msec -1000 0 lO00 msec 3000 FIG. 20. Peristimulus histograms of examplesof single unit activity in All during individual training sessions. A: Cell II-1C developed an increase in both background and evoked activity. B: Cell II-12A developed an increase in background activity and a decrease in evoked activity. The bar from 0-1000 msec indicates the duration of the CS, and the second bar (present only during the conditioning phase) indicates the duration of the US, which was 375 msec. Neuronal activity during the US is not presented for cell II-IC. regions (Diamond and Weinberger, 1984), recording sites located in VE were considered as part of the A I I field. In this paper, we have taken into account the revision of the borders of AII and consider the recording sites in that study to have been in the AII/VE region. Considering that AII/VE is reciprocally connected with AI and the magnocellular M G N (both of which were known to be plastic during learning), it was an appropriate site for providing comparative data on plasticity in different auditory cortical fields. As with the AI and M G N studies, we recorded the activity of single AII/VE neurons during the acquisition of the pupillary dilation CR. Unlike AI and M G N , which are well studied, this was the first account of physiological recordings in AII/VE in behaving animals. As reported above, 63% of the neurons recorded in AI developed response plasticity during learning. In contrast, a significantly greater percentage of cells recorded in AII/VE (95%) developed learning-induced plasticity (Diamond and .Weinberger, 1984). The changes were rapid in development, attaining statistical criterion in an average of 17.3 trials. Examples of peristimulus histograms illustrating plasticity of evoked activity are presented in Fig. 20. Increases and decreases in evoked activity were equally likely (Fig. 21). The reason for the differences between the incidence of plasticity in AII/VE and AI cannot be determined at this time, but it may have resulted from the differential input of the non-plastic lemniscal line to the respective fields. AI is densely innervated by the thalamic lemniscal line (MGv), while AII receives no input from ventral M G N , and VE PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 31 200 150 - i11 (.9 Z it.) Z t/J t.) Og ,,i O- I .---NCEASE o11 TJ | \ o---oDECREASE n=lO /T" J-~"~ T T I 6 I 8 I00 50 0 -50 -100 I 1 I 2 I :5 SENSITIZATION I 1 I 2 l 5 I 4 I 5 I 7 I 9 CONDITIONING BLOCKS OF FIVE TRIALS Fio. 21. Group learning curves of single neurons in AII/VE that developed significant changes in evoked activity during conditioning. Each point is the mean percentage change in evoked activity for blocks of five trials relative to the last five-trial block during the sensitization period. Vertical bars denote +SE. is only sparsely innervated by the ventral subdivision. Perhaps the greater dependence of AII/VE on the diffuse pathway (MGm), which is highly plastic during learning (Section 5), enables these cells to be more sensitive to the acquired significance of acoustic stimuli. 6.3.2. Learning effects in different cortical laminae? One additional finding of single unit activity recorded in AII/VE during learning regards possible differences in the middle cortical lamina as compared to infra- and supra-granular layers. That study revealed an extraordinary degree of physiological plasticity during learning; only one o f 22 neurons recorded in the A I I / V E field was non-plastic. This cell also was unique in three other physiological indices. Specifically, (1) its background activity was significantly higher than that o f the rest of the sample (average of 26 sp/sec vs. 2.5 sp/sec), (2) it was the only cell to exhibit a prolonged excitatory response to the CS (others responded more transiently), and (3) it had a short duration waveform (Fig. 22).* In other studies of unanesthetized animals, the same distinction between cortical cells that have a low background activity, phasic sensory responses and long duration waveforms and those which have a high rate of background activity, sustained evoked responses and short duration spikes is well documented (e.g., Abeles et al., 1975; De Ribaupierre et al., 1972; Goldstein and Abeles 1975; Mountcastle et al., 1969; Simons, 1978). After Mountcastle et al. (1969) we refer to the former class as "regular spikes" and the latter as "thin spikes". These authors have each postulated that thin spike neurons are likely to be stellate cells while regular spike neurons are pyramidal cells. Recently, this speculation was substantiated by McCormick et al. (1985) who were able to relate cellular morphology to electrophysiology in neocortical cells. They demonstrated that thin spikes are unique to aspiny or sparsely spiny GABAergic stellate neurons, while regular spikes are recorded only from pyramidal cells. * Although extracellular recordings of discharge activity cannot unequivocally discern whether the thin spike was recorded from a thalamocortical fiber or a cortical cell, there is good reason to infer that it was somal in origin. Others have discounted the possibility that thin spikes are generated by fibers primarily on the basis of the duration of single unit isolation. According to Mountcastle et at., recordings of single fibers in white matter yielded a very limited spatial extent of recording such that the action potential could be lost with the slightest movement of the electrode. That we were able to isolate and maintain stable recordings of the thin spike for the entire training session (approximately 2 hours) makes it unlikely that the waveform was generated by a fiber. 32 N.M. WEINBERGERand D. M. DIAMOND .a, 13 FIG. 22. Photographs of oscilloscope tracings of two types of extracellularly recorded action potentials in auditory cortex of the cat. A "thin spike" waveform is illustrated at high resolution in the upper left (A). A typical "regular spike" waveform, recorded during another session, is illustrated at the same time base in the lower left. Calibration pulses in the lower left = 0.2 msec. The right side (B) illustrates the background activity of these two neurons. The thin spike cell (upper) discharged 25-30 spikes/sec, and the regular spike cell (lower), like most long duration spikes, discharged less than 2 spike/sec in the absence of acoustic stimulation, (calibration pulses = 0.5 sec in duration). Potentials were filtered at 0.4 kHz high pass and 10 kHz low pass. In a u d i t o r y cortex, this distinction between two types of cortical cells was systematically investigated by Abeles e t al. (1975). They reported that thin spikes are most likely to be e n c o u n t e r e d in the middle cortical layers (III a n d IV), while regular spikes are recorded t h r o u g h o u t the entire extent of cortex. The same l a m i n a r profile of the two cell classes was f o u n d by M o u n t c a s t l e e t al. (1969) a n d Simons (1978) in studies o f single unit activity in s o m a t o s e n s o r y cortex. C o n s i s t e n t with their observations is the well d o c u m e n t e d evidence that m a j o r lemniscal thalamocortical projections terminate in the zone of high concent r a t i o n of stellate cells in layers III a n d IV. T a k e n together, these findings provide suggestive evidence that the thin spike recorded in A I I / V E d u r i n g learning was (i) a stellate cell located in layers II1 or IV; a n d (ii) driven directly by M G N n e u r o n s in the thalamic c o m p o n e n t o f the leminiscal line (ventral M G N ) . * A l t h o u g h speculation based on observations of a single n e u r o n is extremely tenuous, long term recordings from thin spike n e u r o n s are so difficult that there is limited o p p o r t u n i t y for o b t a i n i n g such data in behavioral studies.t Thus, the recording of thin * The prolonged short latency evoked response is not typical of cellular activity in the thalamic lemniscal adjunct (MGdc/MGvl) subdivisions. Their responses tend to be transient onset responses at very long latencies. In contrast, cells in the thalamic lemniscal line (MGv) respond more similarly to the thin spike in the AII/VE. Because All receives no input from MGv, the cell was likely to be in layer Ill or IV or VE, which is a component of the lemniscal line (see Fig. 2). t Thin spike units consistently yield a small signal to noise ratio and have a limited spatial extent over which a single unit can be isolated. The difficulty of holding thin spike activity over long durations is also addressed by Mountcastle et al. (1969) and Simons (1978). PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 33 spike activity during learning appears to have been a rare opportunity to record unit activity from a cortical neuron being driven directly by the thalamus. That the evoked activity of this cell was unchanged during learning suggests that the lack of plasticity in the thalamic lemniscal line may be maintained in the discharge activity of cortical cells directly receiving this input. A lack of callosal inputs to this class of neuron (CipoUini and Peters, 1983) suggests that these cells are not only dominated by non-plastic inputs from the thalamus, but they are also buffered from plasticity which is intrinsic to cortex. The findings from the AII/VE study suggest that certain stellate cells in the middle layer of auditory cortex retain the thalamic representation of sound at a purely "sensory" level.* In contrast, neuronal processing intrinsic to cortex is highly sensitive to the psychological parameters of acoustic stimulation. 6.4. ASSOCIATIVELY-INDUCEDPLASTICITYOF BACKGROUNDACTIVITY In addition to changes in tone evoked responses, a second measure of discharge activity that may be affected by learning is the "spontaneous" or ongoing background activity occurring between training trials (background activity is quantified as the rate of discharge activity during the 1.5 sec preceding the onset of each training trial). Notably, each of the regions of the thalamocortical auditory system that develop plasticity of evoked activity during learning also developed plasticity in background activity.~" Specifically, background activity of neurons in the magnocellular MGN, and cortical fields AI and AII/VE changed during the acquisition of the pupillary dilation CR. The changes in activity cannot be attributed to non-associative arousal because conditioning effects are compared to the preceding sensitization control period. Although the significance of such changes is not known, it is worthwhile to provide some details of changes in background activity, with restrained speculations regarding function. One intriguing finding was that most of the cells (77%) recorded in AII/VE developed decreases in background activity, and the changes occurred at about the same time as the acquisition of behavioral conditioned responses. This result is noteworthy for the following reasons. First, as discussed previously (Section 3.1), there is an overemphasis on the analysis of circuits of motor structures producing what has been considered "the CR". However, a change in cellular activity between trials is occurring in the absence of any conditioned response. Hence, plasticity of background activity underscores the need to view learning as a continuous process occurring at times other than during the presentation of a stimulus or during a response. Second, changes in the background activity of cortical neurons appear to occur only during the initial acquisition phase of learning (e.g. Diamond and Weinberger, 1984; Disterhoft and Olds, 1972; Disterhoft and Stuart, 1976; Dumenko and Sachenko, 1980; O'Brien et al., 1973; Weinberger et al., 1984c). In contrast, studies of auditory cortical activity recorded during the elaboration of a specific somatic conditioned response (Kraus and Disterhoft, 1982) and during the performance of overlearned responses (Beaton and Miller, 1975; Benson and Heinz, 1978; Benson et al., 1981; Goldstein et al., 1982) report that background activity is non-plastic. This indicates that the rapidly acquired conditioned responses involve changes in both background and evoked activity of cells in the auditory thalamus (MGm) and cortex (AI, AII, VE). Following this initial association between the CS and US, background activity appears to remain constant, as additional changes in evoked activity emerge. The later developing plasticity of evoked activity may relate specifically to somatic conditioned responses that are specific to the US (Section 3). * Hyvarinenet al. (1980) reported similar results in a study of the effectsof attention on cellular activityin somatosensorycortex. These authors reported that cellular activityin the middle layer was non-plastic, while activity in the upper and lower layers was sensitiveto the significanceof cutaneous stimuli. t Evoked activity was computed as the differencebetween background discharges and discharges during acoustic stimulation.Hence,plasticityof evokedactivity(discussedpreviously),developedindependentlyof the changes in backgroundactivity. J.PN 2 9 / 1 ~ " 34 N.M. WEINBERGERand D. M. D1AMOND 6.5. A NOTE ON AUDITORY CORTICAL PLASTICITY AND BEHAVIORAL RESPONSES The findings for pupillary behavior and cellular discharges in both the thalamus and auditory cortex indicate rapid changes during conditioning. Is there a causal link between the neuronal plasticity and the pupillary conditioned response? This appears to be extremely unlikely. Although their rates of change are within the same range, a within-subjects analysis reveals a low, non-significant intercorrelation (e.g. Oleson et al., 1975; Ryugo and Weinberger, 1978; Weinberger et al., 1984c; Diamond and Weinberger, 1984; see also Birt and Olds, 1981; and Gabriel et al., 1976). The absence of such findings in the auditory system is not due to limitations of recording techniques or data analysis, because we have obtained high intercorrelations for nuclei that are closely related to efferent control of the pupil (Ashe et al., 1978a; see also Ashe et al., 1978b; and Ashe and Cooper, 1978). Rather, it appears that physiological plasticity in the auditory system during learning is more closely related to learning about the significance of acoustic stimuli than it is to response circuitry. 6.6. SPECIFICITY OF LEARNING-INDUCED CHANGES IN EVOKED ACTIVITY 6.6.1. B a c k g r o u n d Although it is well established that physiological plasticity in auditory cortex is associative in nature, there has been disagreement regarding interpretation of the learning effects. One possibility is that learning merely induces a change in the general excitability of auditory cortical neurons. According to this view, the plasticity is not a reflection of changed processing of information about the conditioned stimulus, per se. On the other hand, the cortical plasticity could be highly specific to the conditioned stimulus. For example, learning-induced changes in evoked activity could reflect a specific "returning" of neurons to the conditioned stimulus. This type of change would entail the systematic development of a narrow band enhancement in sensitivity to the physical characteristics of an important sound. This issue of specificity was addressed by Hall and Mark (1967) and Kitzes et al. (1978). Animals were conditioned to either a tone or light CS, paired with a US. The explicit CS and US were considered to be the only stimuli which provided information to the subjects. "Neutral" probe stimuli (tones) were also presented at a constant rate throughout training. Both studies reported that auditory cortical responses to the probe tones presented during a trial (i.e. immediately following CS onset) were significantly different from responses to these same tones presented between trials. Because these authors assumed that the probe tones had no significance whatsover, they interpreted the change to the probes as indicating a general change in cortical excitability following CS onset. They concluded that learning-induced plasticity in auditory cortex does not reflect a specific change in the processing of the conditioned stimulus. There are two reasons for considering this conclusion unsupported. First, learning effects were compared with a control phase that had isolated tones, without unpaired reinforcement, rather than a sensitization control phase. As the effects might have been nonassociative, these findings do not demonstrate associatively-induced state effects on auditory responses. Second, there was no independent verification that the probe tones were indeed, devoid of significance. On the contrary, the probe tones presented during trials probably did provide salient information. On every training trial, the onset of the CS and US were time-locked to the onset of probe tones. Therefore, the actual conditioned stimulus may have been a compound stimulus consisting of the CS and a constant number of probe tones which provided accurate information concerning the timing of US presentation. Such compound CS and similar temporal conditioning effects are well documented (Pavlov, 1927). In support of this interpretation, Oleson et al. (1975), employing a similar paradigm, reported that subjects oriented to probe stimuli only when they occurred following CS PHYSIOLOGICALPLASTICITYIN AUDITORYCORTEX 35 onset. Given these considerations, the studies discussed above leave the issue of general state effects versus processing specificity unresolved. 6.6.2. Learning-induced changes in frequency receptive fields In order to resolve the processing specificity issue, we recorded the activity of single neurons in All and VE during a modified classical conditioning procedure (Diamond and Weinberger, 1986). In addition to recording evoked activity to the CS during training trials, we also determined the frequency receptive field (FRF) of each neuron before and after the acquisition of a behavioral conditioned response. The F R F provided an index of the sensitivity of AII/VE neurons to a range of different frequencies, only one of which became significant to the animal during learning. The crucial advantage of the F R F determination was that the plasticity which developed during training would be revealed in terms of the frequency specificity of the changes. For example, if learning-induced discharge plasticity merely reflects a general change in excitability, then cells should exhibit either an increase or a decrease in their evoked response across frequencies, without regard to the particular frequency of the CS. On the other hand, if learning induces a specific change in cortical signal processing, then changes in evoked activity should be specific to the frequency of the CS. Upon isolation of discharges from a single neuron, an F R F was obtained by presenting a sequence of isointensity tones (range across sessions: 20-30 ascending frequencies, 0.1-24.0 kHz, 300 msec duration, 1500 msec inter-tone interval, repeated 10-15 times, 30-80 dB) under computer control via a calibrated delivery system. The CS was a tone identical in all parameters (e.g. frequency, intensity and duration) to one of the stimuli used to obtain the FRF. In order to control for non-associative effects, FRFs were determined immediately before and after sensitization (CS/US unpaired, 20 trials), and then after conditioning (CS/US paired, US presented 700-2000 msec after CS offset, 20--45 trials) and extinction (CS alone, 20--40 trials). Stimulus density was maintained constant throughout the training session at an average of 1 stimulus per 20 sec. In each of 20 training sessions, the activity of a single neuron was recorded throughout F R F determinations, sensitization, conditioning, and in some cases, extinction or retention. As with the initial study of AII single unit activity, 95% of the cells rapidly developed discharge plasticity during training. Most importantly, the FRFs of 80% of the neurons were changed following conditioning. To quantify the degree of CS specificity of the changes in FRFs, we calculated a specificity ratio using two frequency range measures: the range of frequencies above and below the CS frequency to which the cell developed discharge plasticity, divided by the total range of frequencies which affected the activity of the cell. Cells with a specificity ratio of <0.5 were classified as frequency specific. Seventy five percent of the plastic cells met this criterion. An example of a cell exhibiting a non-specific associative F R F change is presented in Fig. 23. The post-conditioning F R F indicates an enhancement of inhibition to most frequencies. Examples of specific changes in FRFs are presented in Figs 24 and 25. The FRFs in Fig. 24 illustrate a highly specific increase in evoked activity that developed only to the frequency of the CS (18 kHz). The conditioning effect was then abolished following extinction. This reversal occurred for all cells tested with extinction training. Data from a broadly tuned neuron that developed a specific decrease in evoked activity are presented in Fig. 25. For this training session, the CS was a triad (10, 11, 12kHz). Following sensitization, there was an increased response to numerous frequencies (midddle panel). Despite this increase in evoked activity with sensitization, a highly specific decrease in evoked response to the CS triad developed after conditioning (lower panel). The data illustrate the finding that most of the cells recorded in AII/VE during learning develop highly specific changes in cortical signal processing. In addition, the conditioning effects were reversed following extinction. 36 N . M . WEINBERGER and D. M. DIAMOND it PRE-SENS •• POST-SENS o POST-COND o , ..-., / \ ":~...:*~,::: ....=:..i..ll.~l ~ .et, /\" ,, ; ~"i, ', i' , -,,.....:, ~;..: 'I'." / - '.,=..;j~'~~.| 1215- cs r8 i O I 3 [ I 115 9 12 F R E Q U E N C Y (KHz) 6 ' 118 21 ' 2'4 FIG. 23. Frequency receptive fields (FRFs) of a cell that developed a frequency non-specific decrease in evoked activity with conditioning. The enhancement o f the decrease in evoked response to the CS (18 kHz) also occurred to almost all other frequencies as well. In all F R F analyses, the rate of evoked activity is subtracted from background activity. This difference score provides for quantification of the stimulus evoked inhibition of activity in this figure. To determine the average range of frequency plasticity, post-conditioning data were normalized as a positive percent change from the pre-sensitization FRF, and expressed as the distance, in kHz, from the CS. This analysis revealed that, on the average, the greatest change in evoked activity for frequency specific neurons was at the CS frequency (71%), while the change in response to all other frequencies was less than 25% of control values (Fig. 26). This finding demonstrates that the population of AII/VE neurons developed significant changes in evoked activity during learning that were highly specific to the CS frequency. This normalization analysis was also applied to the post-extinction FRFs. Following extinction, the enhancement of evoked activity with conditioning was no longer present 6050- • PRE-COND _~ o POST-COND 40- POST-EXT >I-. >_- 3020- L) < E3 Ill 10- O > 0- 'g e" ; ill ' ,,: -10 -2O ' 1" CS 0 3 6 [ i 9 12 FREQUENCY (KHz) [ 15 i 18 L 21 FXG. 24. FRFs for a frequency specific neuron located in All. Prior to training, it was excited slightly at frequencies above 8 kHz (pre-cond). Immediately after acquisition of the pupillary dilation conditioned response to a CS of 18 kHz, an excitatory response was increased significantly only at the CS frequency (post-cond). Following behavioral extinction, the increased response at the CS was abolished (post-ext). 37 PHYSIOLOGICAL PLASTICITY IN AUDITORYCORTEX PRE-SENSITIZATION A ILl r.J3 co >I--I-(.3 <E 8 3° l POST-CONDITIONING 0 IJ.I -TO I 0 , 4 L 8 t I'.,~ 12 16 20 FREQUENCY (KHz) FIG. 25. FRF plasticity of a broadly tuned All neuron that developed frequency specific plasticity following conditioning. The FRFs of different phases are presented on separate graphs, and the evoked activity differences from background are shaded solid. In the post-sensitization FRF, the cell exhibited a general increase in excitability to numerous frequencies in the tone sequence. In the post-conditioning FRF, there was a highly specific decrease in evoked activity that occurred only to the three frequency triad (10, 11, 12 kHz) that served as the CS. (Fig. 27, top). There are two possible explanations for this reversal effect: (a) it is attributable to experimental extinction itself; (b) the conditioning effects might be transient, and would have dissipated even in the absence of extinction. A critical test of the "transiency" hypothesis was performed by obtaining "post-retention" FRFs for some 90 --1 ao-1 70 1 FREQ SPECIFIC (n=,,12) FREQ NON-SPECIFIC (n=,,5) -t 60 ,5 50 40 30 20 -10 I o DISTANCE FROM 2 ~ ~ 4 1~ CS (KHz) FIG. 26. Normalized group data for cells classified as CS frequency specific and frequency non-specific in their FRF plasticity. Percent change (associative effect) is given as a percent of distance (kHz) from the CS frequency. The frequency non-specific cells exhibited increased evoked activity across a broad range of frequencies, with no selective accentuation at the CS (0 kHz). In contrast, the frequency specific cells exhibited consistent changes in activity only to the CS frequency. 38 N.M. WEINBERGERand D. M. DIAMOND 9 0 -] 70 60 50 4O z ~ POST-COND (n--7) I m POST-EXTINCTION (n=7) 2 4 A n 1 i i -10 -8 -6 r ~ -4 T -2 0 r 6 T 1 B 10 DISTANCE FROM CS (KHz) 901 [~ POST-COND (n=4) m POST-RETENTION (n~4) 2 4 8O tll (.0 Z < Fzi i i kU O_ -10 -8 -6 -4 -2 0 6 8 10 DISTANCE FROM CS (KHz) FIG. 27. Top: Normalized group data for cells which developed frequency-specificplasticity due to conditioning, and were further studied following extinction. The specificchange at the CS with conditioning was no longer present following extinction training. Bottom: Normalized group data for cells in which a post-retention FRF was obtained. This illustrates that the CS specificchanges in evoked activity due to conditioning did not dissipate spontaneously. cells after an interval of silence equal to the maximum period of time for extinction training (20min). The post-retention F R F summary (Fig. 27, bottom) indicates that frequency specific plasticity was still present; hence, the reversal effect is attributable to extinction, p e r se. The retention test also revealed that learning-induced plasticity persisted for at least 20 min after conditioning was concluded. That the changes outlasted the period of training suggests that these cells are components of short term memory circuitry. Because the plasticity was reversed by extinction, the duration of the memory effect is likely to be a function of the reliability of the CS as a signal for reinforcement. These findings indicate that learning-induced plasticity of cellular responses to a sound reflects specific changes in cortical information processing of that sound. Thus, auditory cortical neurons may function as "adaptive filters" which develop a narrow band change as stimulus meaning or signal value changes during the acquisition of information. 7. Implications of Learning-Induced Plasticity in Auditory Cortex 7.1. INTRODUCTION In the foregoing, we have reviewed our research on learning and physiological plasticity in the auditory system. Although this field is at a relatively early stage of inquiry, it is PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 39 possible to discern some implications of the findings for operation of the neocortex and adaptive functions of the organism. We will take these up in turn. 7.2. IMPLICATIONS OF PHYSIOLOGICAL PLASTICITY FOR CONCEPTIONS OF SENSORY CORTICAL FUNCTION The findings of physiological plasticity, from our laboratory and others, should help to bring about a revision of traditional views of sensory cortical function. An early distinction between functional characteristics of sensory and associational cortices was made by William James (1890), in his analysis of human neurological disorders following brain damage. The prevailing view at that time was that sensory cortical processing is limited solely to stimulus detection (sensation), while cognitive processes such as perception, memory and the capacity to associate meaningful stimuli were assigned to "associational" regions of the neocortex. This distinction was apparently well accepted when Bishop (1958, 1959) provided comparative anatomical data that were interpreted as supportive of the classical view. Based on thalamocortical axon diameters and termination zones, this author viewed primary sensory cortical areas as phylogenetically recent additions to sensory systems. The primary fields were believed to function in the fine discrimination of sensory qualities in higher animals. The output of the sensory cortical areas was believed to extend to phylogenetically older associational cortices, which were thought to integrate sensory information and perform perceptual functions. In the last quarter century, numerous workers have questioned the validity of the traditional view. Diamond and Chow (1962) reviewed neocortical ablation studies in trained animals and concluded that the detection of stimuli, per se, was largely unaffected by extensive removal of sensory cortical areas. The deficits that did occur were better explained by impairment of the ability to learn about complex relations between stimuli. This interpretation has been supported by the findings of later ablation experiments (for reviews, see Elliott and Trahiotis, 1972; Neff et al., 1975; Ravizza and Belmore, 1978). There is an increasing effort to abandon the traditional viewpoint. For example, following a comprehensive review of physiological and ablation studies of the neocortex, Masterton and Berkeley (1974) concluded that the concept of "sensory", "motor" and "associational" cortex has lost most of its original usefulness. They expressed the hope that "these outmoded terms will not be taken seriously by those outside the field, nor, especially, by new generations of students" (p. 295). Merzenich and Kaas 0980) and I. Diamond (1979) proposed similar revisionist views of the utility of the sensory/motor/association cortex distinction. In addition, Diamond (1979) raised a novel point regarding the sensory/motor distinction. He pointed out that cells in layer V throughout the neocortex have direct access to motor effector areas. For example, cells in auditory cortex project to the basal ganglia (Reale and Imig, 1983), deep layers of the superior colliculus (Fries, 1984; Kawamura and Konno, 1979) and also pontine motor nuclei (Andersen et al., 1980; Brodal, 1972; Kawamura and Chiba, 1979). Therefore, Diamond suggested that layer V throughout the neocortex may be considered as the "motor" cortex. His analysis illustrates the current state of dissatisfaction with the standard theory of functional cortical organization. Initial criticisms of the traditional distinction were based primarily on increased knowledge of thalamocortical connectivity and the lack of impairment of purely sensory functions following ablation of sensory cortex. The inadequacy of the traditional view has been further demonstrated by more recent neurophysiological documentation of plasticity in sensory cortex during learning. Although the learning data have come mainly from studies of the auditory cortex, there is also abundent evidence that such plasticity develops in visual and somatosensory neocortex. Therefore, auditory cortex is not a special case; rather, theories of function of sensory neocortex in general require revision.* * Associative effects of learning also develop in the olfactory bulb (Freeman and Schneider, 1982). Therefore, the need to understand sensory processing from the standpoint of learning appears to include the olfactory system as well. 40 N.M. WEINBERGERand D. M. DIAMOND The findings of learning-induced specific changes in frequency tuning of single auditory cortical neurons, (Section 6.6.2) are particularly difficult to reconcile with views that sensory neocortex functions merely as a sensory detector, independent of the salience of stimuli. The plasticity of frequency tuning suggests that auditory cortical cells function as adaptive filters, the output of which is selectively modified during learning. This is directly at variance with the classical distinction between the functioning of sensory and association cortex. New theories of cortical function should not maintain that sensory and association cortex differ qualitatively in their capacity to participate in learning and perception, but should seek other grounds. The dynamic character of auditory, and other sensory, cortex is emphasized by their development of physiological plasticity during learning. These findings are consonent with evidence of dynamic sensory cortical operation obtained in very different circumstances. For example, plasticity of cutaneous receptive fields is evident in somatosensory cortex following removal of a peripheral source of input (Merzenich et al., 1984; Wall et al., 1986). Additionally, adult rats placed in "enriched environments" develop structural plasticity in visual cortex (Rosenzweig et al., 1972; M. Diamond et al., 1985). The latter effects appear to be a result of increased visual learning, per se (Chang and Greenough, 1982; Vrensen and Cardozo, 1981). Despite the extensive and well-established evidence that sensory cortex is highly dynamic, its function is still generally viewed as static, unaffected by learning. For example, according to Fuster (1984), "".. •the participation and purview of primary sensoryareas in memoryis quite differentfrom those of parasensory or associative areas, such as the inferotemporal area. Indeed, there is reason to believe that after certain critical events in early ontogeny, primary areas are relativelyset in their organization. Their cell assemblies are organizedaccording to preprogrammedpatterns of internal and external connectivity,and, as a result, their analytical function is stably determined.It appears that the participation of primary areas in the formation of memory is largelydependent on, if not limited to, their role as the sensory analyzers of what is to have access to memory." (p. 280). Perhaps this ought not to be surprising, because inadequate scientific conceptions seem to be discarded, not in the face of contravening evidence, but only when a better theory is elucidated (Kuhn, 1962). The development of physiological plasticity in sensory cortex during learning provides additional data directly incompatible with the established view. However, no contemporary theory of cortical function has yet replaced the traditional view. 7.3. ADAPTIVE FUNCTIONS 7.3.1. Introduction Whatever the ultimate development of new theories of neocortex, there are sufficient data to consider the adaptive functions of learning-induced cortical plasticity. Three major types of adaptive functions at the level of the organism are usually recognized: (1) stimulus analysis, (2) response or motor-related functions, and (3) cognitive functions. We will consider each in turn. 7.3.2. Stimulus analysis By "stimulus analysis", we refer to processes by which the nervous system accurately responds exclusively to the physical parameters of stimuli. Although neurons in auditory cortex can accurately respond to these parameters, their discharges also are affected by learning, at least in the domain of frequency. Thus, information about both the physical characteristics and the significance of sound may be reflected in their output. Therefore, auditory cortex does not function as a pure sensory analyzer. The distinction between pure sensory analysis and stimulus significance means that physiological plasticity induced during learning is not involved in analysis of the purely physical characteristics of sound. This also implies that the subcortical lemniscal auditory PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 41 system, unaffected by learning, is concerned with stimulus analysis, alone. According to this view, the ventral medial geniculate nucleus is the highest level of the acoustic analysis system. 7.3.3. Response functions Birt and Olds (1981) have argued that learning-induced changes in the auditory system are not directly linked to motor responses, as in a reflex arc. Rather, they hold that this plasticity is involved in some early stages of information processing in which a particular acoustic conditioned stimulus will be linked to a particular conditioned response. Although their argument was based upon physiological plasticity in the region of the magnocellular M G N , rather than auditory cortex, their argument is relevant also for cortical plasticity. A very direct role in motor responses can be eliminated because Birt and Olds found no close relationship between plasticity and conditioned movement in their subjects (rats). Gabriel et al. (1976) also found a poor relationship between physiological plasticity in the magnocellular M G N and avoidance responding, in the rabbit. Likewise, we previously reported a low intra-subject correlation between physiological plasticity in the auditory system (both the magnocellular M G N and the auditory cortex) and conditioned pupillary dilation (Section 6.5). The hypothesis of Birt and Olds that auditory system plasticity represents an early link of the conditioned stimulus to the conditioned response is unlikely to be correct, as it is based on a false assumption. The crux of their argument relies on the assumption that conditioning consists of the acquisition of a single, specific conditioned response. Auditory system plasticity is alleged to be linked to processes that will culminate in the performance of this single response. However, several conditioned responses develop during conditioning. Furthermore, the initial learning, indexed by these CRs, seems to be a stimulusstimulus rather than a stimulus-response association (Section 3.2). Once again, the fact that specific behavioral responses often develop during learning does not mean that learning is simply the development of specific behavioral responses.* 7.3.4. Cognitive functions In this section, we suggest how physiological plasticity that is expressed in sensory cortex during learning may be involved in certain cognitive functions. Specifically, we consider perceptual functions and selective attention. Although stimulus and response processes are more familiar, cognitive processes can also be specified objectively and operationally (e.g. Neisser, 1976), and they are essential to adaptive behavior. An understanding of cortical function at the level of single neurons should provide a basis for understanding its functions as accomplished by the organized neuronal ensembles, circuits, and networks that surely underlie integrated adaptive functions of the entire organism. 7.3.4.1. Perception Perception is not in one-to-one correspondence with the pattern of stimulation on receptor epithelia. Rather, it is a conjoint function of the physical and the psychological parameters of stimuli. The latter include the significance, meaning, and associative links of environmental events, acquired by experience (Epstein, 1967; Gregory, 1974). The role of neocortical plasticity in perception is suggested from the fact that the responses of sensory cortical neurons are affected by both types of stimulus parameters. * Although plasticity in the auditory thalamus and cortex is clearly not involved in a direct sensory-motor circuit, there are connectionsfrom the magnocellularMGN and auditory cortex to motor structures such as the caudate nucleus(LeDouxet al., 1985; Reale and Imig, 1983). However,these connectionsmay be more involved with sensory gating and mechanisms of attention, rather than the generation of motor patterns, per se. (see Lidsky, et al., 1985, for discussion of extra-motor functions of the basal ganglia.) 42 N.M. WEINBERGERand D. M. DIAMOND Learning effects in cortex could subserve perception by providing a basis for stimulus equivalence.* This refers to the fact that organisms regard the same stimulus emanating from the same "object" on different occasions as indeed the "same", despite the fact that the stimuli must be physically different from one time to the next. In the case of the auditory system, the inevitable sources of stimulus variation at the tympanic membrane include changes in distance and position between the sound source and the listener, changes in the position of the head and pinna (external ear) of the listener, and alterations in sound production at the source. Additionally, there may be stimuli from other sources, including those with common frequency spectra which could partially "mask" the sound in question. At the very least, these influences would result in variation in the intensity of a given sound. In other words, in the natural environment, the "same" stimulus is almost never exactly the "same", as transduced at the cochlea. Learning-induced physiological plasticity in auditory cortex could provide a mechanism for stimulus equivalence for important sounds. Learning alters the frequency receptive field with respect to the conditioned stimulus (Fig. 25). Therefore, the "same" sound, i.e. an acoustic stimulus having a particular frequency spectrum, which varies randomly in intensity from one occasion to the next, will engage cells to a greater degree than otherwise, The specificity of plasticity would insure that unimportant stimuli would not receive the same preferential treatment.t This type of mechanism would not compromise the ability of the auditory system to discriminate slight changes in stimulus parameters, should the need for attention to the exact physical characteristics arise, because the subcortical lemniscal system remains sensitive to such fine detail. Cortical plasticity appears to provide a parallel type of processing in which features of stimuli are abstracted, and fine details can be ignored unless of importance. The neurophysiological data upon which this hypothesis is based, as reviewed above, provide independent support for Whitfield's (1979) conclusion that sensory neocortex abstracts objects and concepts from primary sensory data. 7.3.4.2. Selective attention Selective attention refers to the fact that the totality of receptor epithelia of an organism receive continual bombardment; yet, only a fraction of the transduced stimuli actually enter into moderate or long term storage, or guide behavior. A major issue in attention and brain theory is how the processing capacity of the brain is allocated to various stimuli (Kahneman, 1973; Eysenck, 1982). Learning-induced physiological plasticity may play a role in such a process. Learning effectively "recruits" neurons to acoustic conditioned stimuli. This functional recruitment is evident in the specific effects on frequency receptive fields (Fig. 25). Thus, when learning causes more neurons to be "allocated" to the processing of important stimuli, this may signify a literal allocation of processing resources (i.e. neurons) to those stimuli. The amount of selective attention for a stimulus may then be related to the number of neurons which are processing that stimulus. One of the effects of learning at the auditory cortex may then be to provide the basis for the allocation of neurons to the processing of stimuli which, because of their acquired significance, demand more attention. Thus, stimuli-to-be attended will find either more cortical neurons responsive or more responsive cortical neurons. * Stimulus equivalence depends on "object constancy". Object constancy refers to the fact that organisms regard their environment as consisting of some set of stable objects which may move or produce different stimuli (e.g. sounds) at various times, rather than as unique and individual objects whenever they change position or produce new stimuli. Object constancy is a fundamental abstraction about the nature of the environment and is so basic to adaptation that it is often assumed to be "built in" at the time of birth. However, object constancy clearly depends upon experiential factors. Such learning is most clearly seen in developing organisms, such as young children, and its stages have been well documented (Piaget, 1952). t Of course, slight variations in stimulus parameters can be discriminated, if they are within the limits of acuity and if they are important. Nonetheless, they are generally ignored at the gain of dealing with the environment as reasonably stable. PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 43 8. Conclusions We conclude by (a) presenting a brief resume of our findings, (b) considering the relationships among four key topics in neurobiology, and (c) suggesting research avenues that could yield a more coherent understanding of physiological plasticity, cortical function, learning and sensory information processing. 8.1. SUMMARY OF FINDINGS We view learning fundamentally as the acquisition of information and memory as the storage of information. Learning and memory can be inferred from overt behavior, but such effector action is a product of learning rather than the learning itself. Analysis of the rates at which various effector actions change systematically during associative learning reveals a bi-modal distribution. Rapidly-appearing behavioral conditioned responses (e.g. pupillary dilation) develop in all conditioning situations, regardless of the unconditioned stimulus employed. More slowly-appearing effector actions are specific to the unconditioned stimulus. For example, a puff of air to the eye will lead to an eye blink conditioned response and shock to a limb will lead to a flexion conditioned response, but not vice-versa. We interpret these findings to indicate that classical conditioning involves two stages. During the first stage, animals learn that a neutral stimulus predicts or signals a biologically significant stimulus; during the second stage, animals learn to perform a behavioral response that is specific to the unconditioned stimulus. These stages may be characterized, respectively, as stimulus-stimulus (S-S) learning and stimulus-response (S-R) learning. We suspect that S-S learning is necessary for later S-R learning, but this is currently an open question. Nervous system tissue is specialized, not only for the reception, conduction, and transmission of information, but also for expressing systematic change in reactivity under certain circumstances. This physiological plasticity has been studied primarily during development, recovery of function or readjustment following injury and during learning. Learning is more or less a continual process seen both on a long time scale, throughout the lifespan, and on a short time scale, i.e. moment-by-moment acquisition of information. Physiological plasticity is expressed in auditory cortex during learning. Specifically, the responses of cortical neurons to acoustic stimuli change when the importance of the eliciting sound changes. This response plasticity develops rapidly during both nonassociative and associative learning. In non-associative learning, typified by habituation, an acoustic stimulus is merely presented repeatedly and the subject learns that it has no particular significance or value as a signal. Behaviorally, habituation is evident as a systematic decrement in a response to the repeated sound; in our studies, this behavior is pupillary dilation. In associative learning, exemplified by classical conditioning, a sound always precedes a biologically significant stimulus (e.g. food, shock) and the most rapid learning is that the auditory stimulus signals forthcoming reward or punishment. At the behavioral level, this is seen as a systematic increase in pupillary dilation to the sound. In both habituation and classical conditioning, cortical plasticity is specific to the acoustic stimulus; it does not reflect a general change in excitability of the auditory system. Associative plasticity in auditory cortex develops in both the primary (AI) and the secondary/ventral ectosylvian (AII/VE) fields. Within AI, differential plasticity to two sounds that have different meaning is still present after a one week period, and could then be reversed when stimulus significance was reversed. In recordings obtained from one cell throughout a training session, discharge plasticity developed for approximately 60% of neurons in the primary field and virtually all cells recorded in AII/VE. In an initial attempt to understand how physiological plasticity during learning is related to information processing in the auditory cortex, we combined techniques from learning and auditory physiology to study frequency receptive fields of single neurons in cortical areas AII/VE. Frequency receptive fields were altered specifically with respect to the tonal frequency of the conditioned acoustic signal; that is, associative learning can "retune" 44 N.M. WEINBERGERand D. M. DIAMOND neurons and the maximum change is at the frequency of the conditioned stimulus. This demonstrates that the informational content of sound is subject to the effects of learning at the cortical level. Moreover, the change in receptive field did not dissipate spontaneously, but could be reversed quickly by extinction training, in which the acoustic stimulus no longer signals reward or punishment. Thus, the responses of auditory cortex are affected by the psychological parameters of sound (e.g. stimulus significance) as well as by its physical characteristics (e.g. frequency). At the level of the auditory thalamus, sensitivity to these two types of parameters are compartmentalized within two distinct parallel auditory pathways. Non-plastic neurons, which are sensitive to the physical aspects of sound, are in the specific, lemniscal line pathway, e.g. ventral medial geniculate nucleus. In contrast, cells which rapidly develop discharge plasticity, but are insensitive to the details of acoustic parameters, are located in the diffuse auditory pathway, e.g. magnocellular medial geniculate nucleus. Enduring synaptic facilitation can be induced rapidly in this nucleus, as evidenced by long term potentiation. Therefore, the magnocellular MGN may contribute to cortical plasticity during learning. Its diffuse projections to all fields of auditory cortex are consistent with this possibility. These findings underscore the dynamic aspects of sensory cortical function (see also Edelman and Finkel, 1984). They are incompatible with the belief that the auditory cortex is concerned only with analysis of the physical characteristics of sound. Therefore, the traditional distinction between sensory cortex and association cortex appears to be untenable. The adaptive functions of rapid learning-induced physiological plasticity in auditory cortex are not yet known. However, they appear to be neither pure sensory nor motor, but rather higher functions concerned with perception and selective attention. 8.2. RELATIONSHIPSAMONG PHYSIOLOGICALPLASTICITY,NEOCORTICALFUNCTION, LEARNING, AND SENSORY INFORMATIONPROCESSING The rapid expression of physiological plasticity in the auditory cortex during learning links all four areas of inquiry. Here, we discuss some of their relationships. 8.2.1. Physiological plasticity and learning The link between physiological plasticity and learning is secure, both on logical and empirical grounds. The acquisition of information must have a physiological basis for change. Thus, physiological plasticity must underly all learning. Thousands of neurophysiological studies have reported the development of physiological plasticity during learning. So the issue is not "if" there is involvement, but rather the characteristics of the plasticity which underlies learning. In the present chapter, we have pointed out that one of these characteristics is stimulus-specific plasticity in the sensory neocortex of the stimulus whose significance is altered by learning. The neurobiology of learning necessarily entails physiological plasticity. Is the reverse true; that is, does all physiological plasticity involve learning? That is currently unknown. Plasticity will continue to be studied outside of an explicit learning framework, as in development and recovery of function. Both of these processes do take place within organisms as they interact with their environments. Therefore, it is possible that the acquisition of information, i.e. the storage of the effects of stimulation directly from the environment and also that which results from an organism's behavior itself, interacts with processes of development and recovery of function. For example, associative learning is reported to bias the responses of neurons in visual and somatosensory cortices during development (Spinelli and Jensen, 1979). The need for more data is evident. 8.2.2. Auditory cortex, plasticity and learning The idea that cerebral cortex is involved in plasticity and learning is not novel. For example, the traditional view of cortical organization is that association cortex subserves PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 45 learning. But, the development of plasticity in auditory cortex during learning is not part of the standard view of brain function. Traditionally, sensory cortex has been viewed as the highest level of sensory analysis. So entrenched is this view that learning induced physiological plasticity has often been denied (e.g. Miller et al., 1982)* or ignored (e.g. Fuster, 1984). Sensory neurobiology has focused on the fundamental relationships between the physical parameters of stimuli and the responses of neurons within the relevant sensory system. Of course, the resultant data are absolutely essential to an understanding of the functioning of sensory systems. These foundational data are in no sense compromised by the fact that stimulus meaning is also a major factor in the responses of auditory cortical neurons to sound. Learning-induced plasticity must develop within the basic anatomical and physiological framework present within an organism. Physiological plasticity in the auditory cortex reveals dynamic aspects of auditory function specifically, and sensory cortical function in general. Such dynamic characteristics of brain physiology need to be incorporated within a broader, more modern schema than that provided by conceptions limited to rigid stimulus-response formulations. The anatomical roots of auditory plasticity run at least as deep as the magnocellular medial geniculate nucleus. There appears to be something rather basic about the dual functional organization of the auditory thalamus (lemniscal, non-plastic and nonlemniscal, plastic) and its differential projections to the auditory cortex (tonotopic, middle layers and diffuse, upper layers). The duality of information processing in the auditory cortex during learning also raises the question of how the visual and somatosensory systems operate at their cortical levels. In short, sensory neurobiology needs to provide an adequate account of (a) all of the variables that affect information processing in sensory systems and (b) how brains construct or reconstruct an accurate representation of the environment. 8.3. FUTURE DIRECTIONS 8.3.1. Introduction Investigation of cortical neuroplasticity is still in its early stages. Even so, we believe that there is reason to be optimistic about future progress. In particular, we advocate combining two major approaches to information processing in the nervous system, i.e. learning and sensory physiology. Learning can be used to rapidly induce and control physiological plasticity. Sensory physiology can provide a foundation for understanding the functional role of plasticity. Here, we suggest some avenues for future research, beginning with the analysis of cellular mechanisms. 8.3.2. Future Cellular Studies of Physiological Plasticity Our findings offer no direct insights into the cellular bases of physiological plasticity, as they have been limited to determination of the distribution and characteristics of plasticity in the upper auditory system. However, they have provided sufficient information to help guide future experiments at a more reductionistic level. For example, more detailed analysis of physiological plasticity would be facilitated by (a) the ability to control its occurrence, (b) knowledge of its time course, and (c) information on its locus of origin. At this stage, we can provide data relevant to each of this points. Regarding control, plasticity is easily induced by both non-associative (habituation) and associative (conditioning) learning. The use of associative learning during conditioning is probably more desirable than the use of habituation because it is possible to compare physiological processes involved in processing the same acoustic stimulus when that * Miller et al. (1982) attribute changes in evoked activity to the influenceof arousal or attention, rather than learning. For a detailed critique of their proposal, see Diamond and Weinberger (1984) and Weinberger and Diamond, in press. 46 N.M. WEINBERGERand D. M. DIAMOND stimulus is not involved in plasticity, i.e. during the sensitization control period, and when it is so involved, i.e. during CS-US pairing. Additionally, plasticity can be reversed easily by extinction training. Thus, classical conditioning provides a ready means for establishing and abolishing physiological plasticity in auditory cortex. With reference to time course, plasticity is expressed very quickly during CS-US pairing. It does not dissipate spontaneously, but appears to remain until reversed by extinction. The rapid development of plasticity renders it amenable to intracellular investigations. A major question is whether cortical physiological plasticity is local or reflects subcortical plasticity which is projected to the cortex. This traditionial dichotomy does not quite fit into the learning data, because there is evidence that both subcortical and cortical mechanisms are involved. Furthermore, the subcortical effects do not fully account for the characteristics of cortical plasticity; rather, the latter takes on a new form within the cortex. Let us consider each of these points. The thalamic component of the temniscal line (ventral MGN) does not develop physiological plasticity during conditioning (Section 5.1.1 .). This implies that information about the specific physical parameters of sound reaches auditory cortex unaltered by learning. Therefore, learning induced physiological plasticity in auditory cortex cannot be attributed to the subcortical lemniscal line. In contrast, the thalamic component of the diffuse auditory pathway (magnocellular MGN) does develop discharge plasticity rapidly during learning (Sections 5.I.1.). Furthermore, long term potentiation develops in the magnocellular M G N (Section 5.2.), indicating that cells in this nucleus can serve as an origin of plasticity. Notably, the magnocellular M G N projections to auditory cortex are different from those of the thalamic component of the lemniscal line pathway. The diffuse pathway terminates heavily in the upper layer of all auditory cortical fields, while the lemniscal line projects exclusively to the middle layers (Section 4). These findings suggest that learning-induced physiological plasticity in auditory cortex depends upon the confluence of detailed, non-plastic, lemniscal information and plastic, non-lemniscal influences. Biophysical and molecular approaches to cortical physiological plasticity could take into account this framework. For example, the cortical synapses of the magnocellular M G N projection would be an obvious subject of interest. It has previously been determined that these synapses are located on the spines of layer I dendrites, and are likely to be excitatory (Maiskii and Genis, 1972). The fact that the MGv is not plastic is of potential importance because mechanisms of plasticity should not be found in this nucleus. Moreover, the lack of plasticity in the thalamic lemniscal line may be retained by certain neurons in auditory cortex which are directly innervated by MGv (Section 6.3.2). Thus, the MGv and the MGm, as well as their cortical termination zones, could serve as neural substrates for analysis of cellular and subcellular mechanisms of plasticity; commonalities between the two could be discounted as being non-critical for physiological plasticity. In summary, there now appear to be sufficient basic data on the control, time course, and locus to provide for the study of mechanisms of learning-induced physiological plasticity in auditory cortex. 8.3.3. Generality of sensor), cortical plasticity The findings of highly specific effects of learning on frequency receptive fields in AII/VE present a new domain of inquiry for sensory cortex. Just as foundational studies of sensory neurobiology have delineated sensory cortical fields, parallel studies are needed to characterize the involvement of stimulus significance in the responses of sensory cortical neurons. Only by comparative analysis with the visual and somatosensory cortices will it be possible to determine how general or restricted is this cortical plasticity. Within each modality, various fields also need to be compared. For example, it is possible that the multiple sensory fields within each modality differ not only in their responses to the physical parameters of stimuli but also in their sensitivity to the psychological aspects of stimuli. This is the case for AI vs AII/VE (Diamond and Weinberger, 1984). Within each PHYSIOLOGICAL PLASTICITY IN AUDITORY CORTEX 47 modality, various stimulus parameters need to be studied during learning. In the auditory system, in addition to frequency, location of the sound source would be particularly appropriate, given the sensitivity of auditory cortical cells to binaural cues (e.g. Kitzes et al., 1980). 8.3.4. The basic paradigm of neurobiology The basic paradigm of contemporary neurobiology can be traced rather directly to the sensory-motor foundations of this science in the 18th and 19th centuries (Boring, 1957; Liddell, 1960; Fearing, 1964; Young, 1970). Although experimental neurobiology based on this paradigm has expanded in a systematic and productive manner, there are too many aspects of brain function which cannot be encompassed therein. While we cannot recount the contrary findings here, we have at least pointed out how traditional conceptions of sensory cortex are incompatible with a growing body of anatomical and physiological data. The new theme is the dynamic character of sensory cortex. Our findings emphasize the fact that this dynamism is evident on a moment-by-moment time scale. It would be premature and presumptuous to attempt to formulate a new paradigm for brain organization at this time. We do urge that a great deal more research be devoted to neuroplasticity, in order to achieve a comprehensive characterization of how this property is distributed throughout the brain, the circumstances under which it is expressed, and its biophysical and molecular bases. One avenue is to explicitly control the expression of plasticity using learning. The techniques for instituting and controlling learning are neither mysterious nor forbidding. They could, with profit, be included routinely in the gamut of techniques that are part of normative neurobiology. Further, since plasticity is expressed whenever information is acquired, and since information acquisition is a pervasive process, it is important to recognize that learning-induced plasticity may be instituted even when an experimenter has no intentions of so doing, This hightened awareness should help focus attention on the dynamic aspects of brain function. Acknowledgements This work has been supported by research grants MH 11250, 22712 BNS 81924, NS 16108, ONR N-00014-84-K-0391, fellowships MH 05424, 05440, 11095, and 51342, training grants MH 11095 and 14599, an unrestricted grant from the Monsanto Company, and the University of California Irvine Focused Research Program in Cooperative Brain Function. DD was supported in part by a UC Irvine Dissertation Fellowship and Chancellor's Patent Fund Award. We wish to thank Drs. J. Michael Cassady and Herman Birch for writing computer programs and Jacquie Weinberger for invaluable secretarial services. 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