Download Morphological Basis of Learning and Memory: Vertebrates

Morphological Basis of Learning and Memory: Vertebrates
The central issue in morphology of learning and memory is how such constructs are stored in the nervous system.
The basic shape of neurons and synapses is illustrated in Figure 1. In the late nineteenth century, Santiago Ramón y Cajal
suggested that learning might involve changes in the synaptic connections through which neurons communicate. Such
synaptic change could take at least four possible forms. First, the pattern of functional connections could be altered by
forming new synapses or removing existing synapses. Second, the pattern of functional connections could be altered by
selectively strengthening or weakening some synapses. There is very strong evidence for both of these possibilities
during learning, and in models of learning such as Long-Term Potentiation. Third, neuronal circuits could be modified
by forming new neurons (neurogenesis), for which there is growing support suggesting that neurogenesis is possible in
the mature brain. Fourth, changes could occur in both the nonsynaptic regions of neurons and in nonneural elements of
the brain such as glial cells and the vascular system subserving the brain, for which there is evidence as well.
Changes in Synapse Number
Important roots of memory research lie in studies of the effects of experience upon brain development. For example,
visual experience is necessary to develop normal visual ability in mammals. Searching for a basis for this in brain
anatomy, Cragg (1975), and others, noted that animals deprived of visual experience had fewer synaptic connections per
nerve cell in visual cortex. These studies profoundly influenced thinking about the processes by which the brain stores
information, because they showed that (1) brain structure is malleable; (2) synaptic organization can be orchestrated into
different configurations by behavioral experience; (3) both forming new connections and pruning existing connections
are involved in altering brain organization; (4) differential experience can modify the structure of synapses, suggesting
that synaptic efficacy (or strength) also can change. These effects of experience on synaptic connections during
development led to proposals that such changes might underlie adult learning as well.
A separate developmental approach that was very fruitful in understanding brain substrates of learning and memory
involved enriching young animals’ lives with additional stimulation. Donald Hebb proposed ways in which synaptic
change could be incorporated meaningfully into functional circuitry. With his students, he showed that enriching the
rearing environment of rats with cagemates and toys improved the animals’ ability to solve complex problems. Hebb
concluded that behavior, and by implication brain organization, was permanently altered by this early experience.
Subsequently, Rosenzweig, Bennett and Diamond (1972) found that regions of the cerebral cortex were thicker and
heavier in rats reared in enriched environments, compared with rats reared in solitary or group cages. Volkmar and
Greenough (1972) followed up these findings, reporting that visual cortical neurons of rats reared in enriched
environments had larger dendritic fields than did those of cage housed controls. Dendrites of neurons receive the bulk of
the synaptic input (see Figure 1), so the implication was that new synapses formed. Similar findings were subsequently
reported in other areas of the cerebral cortex and in brain regions such as hippocampus, superior colliculus, and
cerebellum. Of particular importance to learning and memory was that the enriched environment changed brain anatomy
in adult rats. Turner and Greenough (1985) found that rats reared in enriched environments had more synapses per
neuron in the visual cortex, compared with rats reared alone or in pairs in standard laboratory cages. Moreover, similar
changes occur in the striatum as well (Comery, Shah and Greenough, 1995), suggesting that the experience-dependent
changes in neuronal morphology influence multiple levels/systems in the brain. The general conclusion from the
enriched environment studies is that when animals are placed in an environment in which they store information that
affects later behavior, they form new synapses. (The term “enriched” is used in comparison with the typical laboratory
environment and does not imply superiority to the natural environment.)
Follow-up studies have explored the effects of specific learning tasks upon these same measures. There is
compelling evidence that many forms of learning change both the amount of dendrite per neuron and the number of
synapses per neuron. For example, dendritic branching is increased in visual cortical neurons following 25 days’
exposure to a series of maze problems (Greenough, Juraska and Volkmar, 1979). Subsequent work used “split-brain”
rats, severing the nerve fibers that allow the right and left hemispheres to communicate, and opaque contact lenses that
restricted visual input from training to one eye. Neurons on the “trained” side of the brain selectively exhibited dendritic
field size increases; thus these changes were not of the general sort that might be due to stress or arousal associated with
the task, which should affect both sides of the brain equally. These studies, and others, indicated that the altered dendritic
fields were associated with neural input and output related to the training.
Synaptogenesis is also implicated in associative learning. Tsukahara (1981) investigated associative limb flexion
conditioning, using stimulation to the cerebral peduncle as the conditioned stimulus and electric shock to the forelimb as
the unconditioned stimulus. In this paradigm, red nucleus lesions abolish the conditioned response, implicating the
involvement of this structure. Electrophysiological studies following conditioning indicated enhanced input to the red
nucleus from the cerebral cortex. Subsequently, Tsukahara’s coworkers Murakami et al. (1987) reported morphological
evidence for formation of new corticorubral synapses in conditioned animals.
Similar anatomical effects of training have been observed in other behavioral tasks. Stewart (1991) examined dayold chicks that learned to avoid pecking a bad-tasting food particle and found an elevated number of synapses in a
forebrain region previously shown to be involved in the learning. In another involved brain region, there were also
increases in the number of spines (see Figure l), the dendritic component of one type of synapse. Likewise, the density of
spines was reported to be elevated in hippocampal area CA1 in adult rats following spatial learning (Moser, Trommald,
and Andersen, 1994). Comparable changes have been observed in numerous other paradigms (e.g., Bird Song Learning
and Imprinting in birds). Finally, while this discussion is confined to vertebrates; there is excellent evidence for
comparable synaptic number changes in invertebrate plasticity paradigms (see “Invertebrates,” above).
Neurogenesis presents yet another mechanism whereby brain organization could be modified in response to
experience. For example, increased numbers of new neurons have been reported in the dentate gyrus of the hippocampus
of animals exposed to a complex environment (Kempermann, Kuhn and Gage, 1998) or permitted access to an exercise
wheel (van Praag et al., 1999). More specific to learning and memory, Gould et al. (1999a) reported that the number of
“new” neurons in rat dentate gyrus dramatically increased following associative conditioning. While most reports of
neurogenesis have been limited to rodents and to relatively unique areas in the brain such as hippocampus or olfactory
bulb, neurogenesis has been reported in primate cortex (Gould et al., 1999b) and disputed (Kornack and Rakic, 2001).
While the issues of the extent, location, and functional relevance of neurogenesis has yet to be resolved, the mere
possibility of such a mechanism in the mature central nervous system has spurred a great deal of excitement and hope for
many areas of neural research.
An issue that affects all of these studies is whether anatomical changes that are seen following training result merely
from increased neural activity involved in performing the learned task. Muscles grow larger in response to exercise;
perhaps neurons do too, such that these structural changes have nothing to do with learning or memory per se. (This issue
is, of course, not unique to morphological studies; proposed molecular and other aspects of the cellular mechanisms of
memory may similarly be artifacts of activity; see Protein Synthesis in Long-Term Memory in Vertebrates.)
There have been direct tests of the effects of neural activity versus learning on synapse change. For example, Black
et al. (1990) compared a cerebellar cortical region in rats that had learned a complex series of motor tasks to that of rats
that performed one of two forms of physical exertion involving little learning: running on a treadmill or in an activity
wheel. Exercise alone had no effect on synapse number whereas rats that had learned increased the number of synapses
per neuron in the cerebellar cortex. Similar effects have been observed in areas such as motor cortex (Kleim et al., 1996).
In contrast, blood vessel density was elevated in affected regions in rats that had exercised, whereas the motor learning
rats had the same blood vessel density as control animals. These results indicate that activity and learning have very
different effects on brain tissue.
Additional support for the role of synapse formation in plastic neural change has come from studies of long-term
potentiation. LTP involves an increase in the response of postsynaptic neurons following high-frequency bursts of
presynaptic firing. LTP induction has been shown to increase spine density and modify dendritic morphology in motor
cortex (Ivanco, Racine and Kolb, 2000) and in hippocampal subfield CA1 (Lee et al., 1981). Chang and Greenough
(1984) included a type of high-frequency stimulation that did not induce LTP, and subsequently did not alter synapse
formation, suggesting that LTP-induced synaptogenesis was not caused by high-frequency stimulation alone. In contrast
to CA1, induction of LTP in the hippocampal dentate gyrus does not cause synapse formation but changes synapse
structure (Geinisman, de Toledo-Morrell, and Morrell, 1991). Recent advances in microscopy have permitted near realtime evaluation of changes in morphology of living neurons. Using these tools, Engert and Bonhoeffer (1999) reported
that postsynaptic spines are formed de novo in response to stimulation. Taken together, these findings reinforce the view
that numerous different cellular changes may be involved in learning, and other forms of neural plasticity as well.
Changes in Synapse Structure: Indications of Synapse Efficacy Change
Several structural features of synapses have been found to be altered by behavioral experience. One of the most
obvious features is the size of synapses. Larger synapses may release more neurotransmitter or have more receptors, such
that a size change could indicate a strength change. Early findings indicated smaller synapses in visual cortex of animals
visually deprived during development, and Tieman (1985) reported smaller geniculocortical synapses in monocularly
deprived cats. Conversely, synapse size increased following imprinting in day-old chicks, and similar size changes were
found after avoidance learning. Larger synapses were also observed in layer IV of visual cortex of rats reared in enriched
environments, compared with individually caged controls. Likewise, changes in the size of synaptic spine heads and
necks (see Figure 1) have been described by Van Harreveld and Fifkova (1975) following LTP induction in dentate
gyrus. It has been suggested that larger spine components, and the associated spine neck restriction, may permit
activation of focalized intracellular cascades (Koch and Zador, 1993) and reduce electrical resistance, thereby facilitating
the passage of synaptic current into the dendrite (Harris and Kater, 1994).
Synaptic vesicles are believed to contain neurotransmitters, and changes in their numbers could indicate changes in
synapse strength. Synaptic vesicle numbers have been reported to decrease with visual deprivation (e.g., Tieman, 1985)
and there have are reports of both decreased vesicle density and altered vesicle location within the presynaptic terminal
following LTP induction (e.g., Fifkova and Van Harreveld, 1977; Applegate, Kerr, and Landfield, 1987). In contrast,
vesicle numbers have been shown to increase in rats reared in enriched environments (Sirevaag and Greenough, 1991;
Nakamura et al., 1999).
At least three other synapse features appear to be sensitive to experience. First, small discontinuities in the
postsynaptic density, termed perforations, have been found to increase in number following complex environment
exposure and to decrease in affected synapses subsequent to sensory deprivation (Greenough, West, and DeVoogd,
1978). Moreover, Vrensen and Cardozo (1981) found that the number of perforated synapses increased in the visual
cortex following visual discrimination learning. The function of these perforations is unknown. Second, the incidence of
multiple synaptic boutons (presynaptic elements that synapse with multiple postsynaptic components) is elevated in
numerous brain areas following exposure to a complex environment and in animals that have been trained in an
associative learning paradigm (Jones et al., 1997; Geinisman et al., 2001). Third, the cellular organelles that synthesize
protein, polyribosomal aggregates, are frequently found in the heads and necks of spines during periods of synapse
formation (Steward and Falk, 1985). They are also found more frequently in spines of animals in complex environments,
possibly reflecting greater rates of synapse formation. Protein synthesis at the synapse has been proposed as a memory
mechanism (Steward and Schuman, 2001).
Changes in Nonneural Elements
The enriched environment work indicated from its earliest days that morphological changes were not restricted to
neurons. Glial cells, supportive elements that maintain ionic, metabolic, and neurotransmitter homeostasis, respond
similarly to environmental complexity. Sirevaag and Greenough (1991) reported that astrocytes grow larger and extend
additional processes into the tissue during the first phase of their response to the animal’s housing in an enriched
environment. In a second phase, astrocytes divide, increasing their numbers, and shrink, on average, toward their preexposure size. These stages are qualitatively comparable with those of gliosis, the glial reaction to injury, yet protracted.
Moreover, Anderson et al. (1994) have shown an increase in the volume of glia per Purkinje cell in the cerebellum of
animals that learned a complex motor skill, but not those who simply exercised. Likewise, blood vessel density increased
in rats placed in an enriched environment at the age of weaning. In animals that are older at the time they are first
exposed to enrichment, this blood vessel response diminishes with increasing age.
Conclusions
Morphological research has provided strong evidence for both forms of synaptic change that have been proposed to
underlie learning and memory. Formation, and occasionally loss, of synapses occurs both during periods of development
when the brain is storing information and during exposure to specific learning tasks. Various control procedures have
largely ruled out the possibility that these synaptic changes are artifactual results arising from factors other than learning.
Changes in the structure of synapses, such as in the size or shape of synaptic components, also occur during learning and
in other situations in which functional brain organization is altered, such as LTP. Many of these structural changes have
been associated with synapse strength differences in other research. Recent, still developing research implicates addition
of new neurons via neurogenesis and changes in non-neuronal elements of the brain. Thus the weight of the evidence
indicates that both synapse formation/removal and synapse strength changes are involved in learning and memory, but
additional mechanisms are also likely.
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William T. Greenough
James D. Churchill
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