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Ling 411 – 19 Learning REVIEW Operations in relational networks Relational networks are dynamic Activation moves along lines and through nodes Links have varying strengths • A stronger link carries more activation, other things being equal All nodes operate on two principles: • Integration Of incoming activation • Broadcasting To other nodes Review Operation of the Network in terms of cortical columns The linguistic system operates as distributed processing of multiple individual components • “Nodes” in an abstract model • These nodes are implemented as cortical columns Columnar Functions • Integration: A column is activated if it receives enough activation from other columns Can be activated to varying degrees Can keep activation alive for a period of time • Broadcasting: An activated column transmits activation to other columns Exitatory – contribution to higher level Inhibitory – dampens competition at same level Additional operations: Learning Links get stronger when they are successfully used (Hebbian learning) • Learning consists of strengthening them • Hebb 1948 Threshold adjustment • When a node is recruited its threshold increases • Otherwise, nodes would be too easily satisfied Neural processes for learning Basic principle: when a connection is successfully used, it becomes stronger • Successfully used if another connection to same node is simultaneously active Mechanisms of strengthening • Biochemical changes at synapses • Growth of dendritic spines • Formation of new synapses Weakening: when neurons fire independently of each other their mutual connections (if any) weaken Neural processes for learning C Synapses here get strengthened A B If connections AC and BC are active at the same time, and if their joint activation is strong enough to activate C, they both get strengthened (adapted from Hebb) Requirements that must be assumed (implied by the Hebbian learning principle) Prerequisites: • Initially, connection strengths are very weak Term: Latent Links • They must be accompanied by nodes Term: Latent Nodes • Latent nodes and latent connections must be available for learning anything learnable The Abundance Hypothesis • Abundant latent links • Abundant latent nodes Abundance is a property of biological systems generally Cf.: Acorns falling from an oak tree Cf.: A sea tortoise lays thousands of eggs • Only a few will produce viable offspring Cf. Edelman: “silent synapses” • The great preponderance of cortical synapses are “silent” (i.e., latent) Electrical activity sent from a cell body to its axon travels to thousands of axon branches, even though only one or a few of them may lead to downstream activation Learning – The Basic Process Latent nodes Latent links Dedicated nodes and links Learning – The Basic Process Latent nodes Let these links get activated Learning – The Basic Process Latent nodes Then these nodes will get activated Learning – The Basic Process That will activate these links Learning – The Basic Process This node gets enough activation to satisfy its threshold Learning – The Basic Process This node is therefore recruited B A These links get strengthened and the node’s threshold gets raised Learning – The Basic Process This node is now dedicated to function AB AB B A Learning Next time it gets activated it will send activation on these links to next level AB B A Learning: more terms AB Child nodes Potential Actual B A Parent nodes Learning: Deductions from the basic process Learning is generally bottom-up The knowledge structure as learned by the cognitive network is hierarchical — has multiple layers Hierarchy and proximity: • Logically adjacent levels in a hierarchy can be expected to be locally adjacent Excitatory connections are predominantly from one layer of a hierarchy to the next Higher levels will tend to have larger numbers of nodes than lower levels Learning in cortical networks: A Darwinian process The abundance hypothesis • Needed to allow flexibility of learning • Abundant latent nodes Must be present throughout cortex • Abundant latent connections of a node Every node must have abundant latent links A trial-and-error process: • Thousands of connection possibilities available The abundance hypothesis • Strengthen those few that succeed Cf. natural selection “Neural Darwinism” (Edelman) Anatomical support for the hypothesis of abundant latent links A typical pyramidal node has • thousands of incoming synapses connecting to its dendrites and its cell body • thousands of output synapses from multiple branches of its axon But only a very few of these are recruited for a specific function • For example, the typical node in a functional web has perhaps only dozens or maybe up to 100 or so links By far the great preponderance of these are latent • Edelman: “silent synapses” Learning – Enhanced understanding This “basic process” is not the full story The nodes of the above depiction: • Are they minicolumns, maxicolumns, or what? • Most likely, a bundle of contiguous columns • Often a maxicolumn or hypercolumn REVIEW Columns of different sizes Minicolumn • Basic anatomically described unit • 70-110 neurons (avg 75-80) • Diameter barely more than that of pyramidal cell body (30-50 μ) Maxicolumn (term used by Mountcastle) • Diameter 300-500 μ • Bundle of 100 or more contiguous minicolumns Hypercolumn – up to 1 mm diameter • Can be long and narrow rather than cylindrical • Bundle of contiguous maxicolumns Functional column • Intermediate between minicolumn and maxicolumn • A contiguous group of minicolumns REVIEW Hypercolums: Modules of maxicolumns A homotypical area in the temporal lobe of a macaque monkey Functional columns vis-à-vis minicolumns and maxicolumns Maxicolumn • About 100 minicolumns • About 300-500 microns in diameter Functional column • A group of one to several contiguous minicolumns within a maxicolumn • Established during learning • Initially it might be an entire maxicolumn Learning in a system with columns of different sizes At early learning stage, maybe a whole hypercolumn gets recruited Later, maxicolumns for further distinctions Still later, functional columns as subcolumns within maxicolumns New term: Supercolumn – a group of minicolumns of whatever size, hypercolumn, maxicolumn, functional column • Any supercolumn is potentially divisible Links between supercolumns will thus consist of multiple fibers REVIEW Functional columns in phonological recognition: A hypothesis Demisyllable (e.g. /de-/) activates a maxicolumn Different functional columns within the maxicolumn for syllables with this demisyllable • /ded/, /deb/, /det/, /dek/, /den/, /del/ REVIEW Functional columns in phonological recognition A hypothesis deb [de-] ded det de- den del dek A maxicolumn (ca. 100 minicolumns) Divided into functional columns (Note that all respond to /de-/) Functional columns in phonological recognition A hypothesis deb [de-] ded det de- den del dek This one learned first Then, subdivisions are established REVIEW Adjacent maxicolumns in phonological cortex? Hypercolum de- te- be- pe- ge- ke- A module of contiguous maxicolumns Each of these maxicolumns is divided into functional columns Note that the entire module responds to [-e-] Revisit the basic learning diagram: Let each node represent a supercolumn Latent supercolumns Bundles of latent links Dedicated supercolumns and links Learning – The Basic Process Let these links get activated Learning – The Basic Process: Refined view Then these supercolumns get activated Learning – The Basic Process: Refined view That will activate these links Learning – Refined view This supercolumn gets enough activation to satisfy its threshold Learning – Refined view This supercolumn is recruited for function AB AB B A Learning: Refined view Next time it gets activated it will send activation on these links to next level AB B A Learning Refined view Can get subdivided for finer distinctions AB B A Learning: Refined view AB Hypercolumn composed of 3 maxicolumns – Can get subdivided for finer distinctions B A A further enhancement Minicolumns within a supercolumn have mutual horizontal excitatory connections Therefore, some minicolumns can get activated from their neighbors even if they don’t receive activation from outside Learning: refined view If, later, C is activated along with A and B, then maxicolumn ABC is recruited for ABC ABC AB B A C Learning: refined view And the connection from C to ABC is strengthened –it is no longer latent ABC AB B A C Learning phonological distinctions: A hypothesis deb ded det de- de- te- be- pe- ge- ke- den del dek 3. The maxicolumn gets divided into functional columns 2. It gets subdivided into maxicolumns for demisyllables 1. In learning, this hypercolumn gets established first, responding to [-e-] Remaining question – learning lateral inhibition When a hypercolumn is first recruited, no lateral inhibition among its internal subdivisions • (Or very little) Later, when finer distinctions are learned, they get reinforced by lateral inhibition • Latent inhibitory neurons become activated Question: How does this process work? • I.e., what makes these inhibitory neurons change from latent to active? “Evolutionary Learning” and the Proximity Principle Related functions tend to be in close proximity • If very closely related, they tend to be adjacent Areas which integrate properties of different subsystems (e.g., different sensory modalities) tend to be in locations intermediate between those subsystems Evolutionary Learning and the Proximity Principle Start with the observation: • Related areas tend to be adjacent to each other Primary auditory and Wernicke’s area V1 and V2, etc. Wernicke’s area and lexical-conceptual information – angular gyrus, MTG • Thus we have the ‘proximity principle’ Question: Why – How to explain? How to Explain the Proximity Principle? Factors responsible for observations of proximity in cortical structure 1. Economic necessity 2. Genetic factors 3. Experience – provides details of localization within the limits imposed by genetic factors Proximity: Economic necessity Question: Could a given column be connected to any other column anywhere in the cortex? That would require a huge number of available latent connections Way more than are present Hence there are strict limits on intercolumn connectivity Therefore, proximity is necessary just for economy of representation Limits on intercolumn connectivity Number of cortical minicolumns: • If 27 billion neurons in entire cortex • If avg. 77 neurons per minicolumn • Then 350 million minicolumns in the cortex Extent of available latent connections to other columns • Perhaps 35,000 to 350,000 • Do the math.. A given column has available latent connections to between 1/1000 and 1/10000 of the other columns in the cortex Locations of available latent connections Local • Surrounding area • Horizontal connections (grey matter) Intermediate • Short-distance fibers in white matter • For example from one gyrus to neighboring gyrus Long-distance • Long-distance fiber bundles • At ends, considerable branching The role of long-distance fibers Arcuate fasciculus • Genetically determined • Limits location of phonological recognition area Interhemispheric fibers • Also genetically determined • Wernicke’s area – RH homolog of W’s area • Broca’s area – RH homolog of B’s area • Etc. Cortical connectivity properties (Cf. Pulvermüller 2002:17) Probability of adjacent areas being connected: >70% • But if we count by columns instead of cells the figure is probably higher, maybe close to 100% Probability of distant areas being connected: 15-30% • Distant areas: at least one intervening area • In Macaque monkey, most areas have links to 10 or more other areas within same hemisphere Cortical connectivity properties Probability of adjacent areas being connected: >70% (Pulvermüller p. 17) • But if we count by minicolumns instead of cells the figure is probably higher, maybe close to 100% Probability of distant areas being connected: 15-30% (p. 17) • Distant areas: at least one intervening area • In Macaque monkey, most areas have links to 10 or more other areas within same hemisphere More cortical connectivity properties Most areas are connected to homotopic area of opposite hemisphere Most connections between areas are reciprocal Primary areas not directly connected to one another, except for motor-somatosensory • Connections under central sulcus Degrees of separation between cortical neurons or columns For neurons of neighboring columns: 1 For distant neurons in same hemisphere • Range: 1 to about 5 or 6 (estimate) • Mostly 1, 2, or 3, especially if functionally closely related • Average about 3 (estimate) For opposite hemisphere • Add 1 to figures for same hemisphere Probably, for any two columns anywhere in the cortex, whether functionally related or not, fewer than 6 degrees of separation Some long-distance fiber bundles (schematic) Two Factors in Localization Genetic factors determine general area for a particular type of knowledge Within this general area the learning-based proximity factors select a more narrowly defined location Thus the exact localization depends on experience of the individual When part of the system is damaged, learning-based factors can take over and result in an abnormal location for a function – plasticity Genetically determined proximity Genetically-determined proximity would have developed over a long period of evolution • Many features are shared with other mammals This process could be called ‘evolutionary learning’ According to standard evolutionary theory.. • A process of trial-and-error: Trial • Produce varieties Error: • • Most varieties will not survive/reproduce The others – the best among them – are selected Other genetic factors supplement proximity • Long-distance fiber bundles Some innate factors relating to localization Primary areas Long-distance fiber bundles Innate factors relating to primary areas Location • Genetically determined locations But there are exceptions • Malformation • Damage Structure • Genetically determined structures adapted to sensory modality (they have to be where they are) Heterotypical structures • Found in primary areas Primary visual Primary auditory REVIEW A Heterotypical (i.e., genetically built-in) structure Visual motion perception An area in the posterior bank of the superior temporal sulcus of a macaque monkey (“V-5”) A heterotpical area Albright et al. 1984 REVIEW A Heterotypical structure: Auditory areas in a cat’s cortex A1 AAF – Anterior auditory field A1 – Primary auditory field PAF – Posterior auditory field VPAF – Ventral posterior auditory field Innate factors relating to localization The primary areas Long-distance fiber bundles • Interhemispheric – via corpus callosum • Longitudinal – from front to back Arcuate fasciculus is part of the superior longitudinal fasciculus They allow for exceptions to proximity • Areas closely related yet not neighboring Implications of the proximity principle System level • Functionally related subsystems will tend to be close to one another • Neighboring subsystems will probably have related functions Cortical column level • Nodes for similar functions should be physically close to one another • Nodes that are physically close to one another probably have similar functions Therefore.. • Neighboring nodes are likely to be competitors • They need to have mutually inhibitory connections Applying the proximity principle For both types (genetic and experience-based) we can make predictions of where various functions are most likely to be located, based on the proximity principle • Broca’s area near the inferior precentral gyrus • Wernicke’s area near the primary auditory area Such predictions are possible even in cases where we don’t know whether genetics or learning is responsible • maybe both Deriving location from proximity hypothesis The cortex has to provide for “decoding” speech input Speech input enters the cortex in the primary auditory area Results of the “decoding” (recognition of syllables etc.) are represented in Wernicke’s area Why is Wernicke’s area where it is? Speech Recognition in the Left Hemisphere Phonological Production Primary Auditory Area Phonological Recognition Wernicke’s Area Exercise: Location of Wernicke’s area Why is phonological recognition in the posterior superior temporal gyrus? • Alternatives to consider: Anterior to primary auditory cortex • Advantage: would be close to phonological production Inferior to primary auditory cortex (There are two reasons) Answer: Location of Wernicke’s area Wernicke’s area pretty much has to be where it is to take advantage of the arcuate fasciculus The location of W.’s area makes it close to angular gyrus, likely area for noun lemmas (morphemes and complex morphemes) Also, close to SMG, presumed area for phonological monitoring • (Why? Because it is adjacent to primary somatosensory area) More exercises Explaining likely locations of morphemes • verb morphemes in the frontal lobe • noun morphemes in the angular gyrus and/or middle temporal gyrus The dorsal (where) pathway of visual perception Experience-based proximity Can be expected to be operative • more at higher (more abstract) levels, less at lower levels • for areas of knowledge that have developed too recently for evolution to have played a role Reading Writing Higher mathematics Physics, computer technology, etc. Innate features that support language Columnar structure Coding of frequencies in Heschl’s gyrus Arcuate fasciculus Interhemispheric connections (via corpus callosum) – e.g., connect Wernicke’s area with RH homolog Spread of myelination from primary areas to successively higher levels Left-hemisphere dominance for grammar etc. Consequences of the Proximity Principle Nodes in close competition will tend to be neighbors • And their mutual competition is preordained even though the properties they are destined to integrate will only be established through the learning process Therefore, inhibitory connections should exist predominantly among nodes of the same hierarchical level • Confirmed by neuroanatomy • The presence of their mutual inhibitory connections is presumably specified genetically Variation in threshold strength N.B. All of these properties are found in neural structures Thresholds are not fixed • They vary as a result of use – learning Nor are they integral What we really have are threshold functions, such that • A weak amount of incoming activation produces no response • A larger degree of activation results in weak outgoing activation • A still higher degree of activation yields strong outgoing activation • S-shaped (“sigmoid”) function Outgoing activation Threshold function --------------- Incoming activation ------------------- end