Rice LING 411 - The Proximity Principle and Evolutionary Learning

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(1) Learning (2) The proximity principle and “evolutionary learning”Schedule of PresentationsOperations in relational networksOperation of the Network in terms of cortical columnsAdditional operations: LearningRequirements that must be assumed (implied by the Hebbian learning principle)Support for the abundance hypothesisLearning – The Basic ProcessSlide 9Slide 10Slide 11Slide 12Slide 13Slide 14LearningLearning: more termsLearning: Deductions from the basic processLearning in cortical networks: A Darwinian processLearning – Enhanced understandingColumns of different sizesHypercolums: Modules of maxicolumnsFunctional columns vis-à-vis minicolumns and maxicolumnsLearning in a system with columns of different sizesQuestion on cortical columnsFunctional columns in phonological recognition: A hypothesisFunctional columns in phonological recognition A hypothesisPhonological hypercolumns (a hypothesis)Adjacent maxicolumns in phonological cortex?Adjacent maxicolumns in phonological cortex?Revisit the diagram: Each node of the diagram represents a group of minicolumns – a supercolumnSlide 31Learning – The Basic Process: Refined viewSlide 33Learning – Refined viewSlide 35Learning: Refined viewLearning Refined viewA further enhancementLearning: Refined viewLearning: refined viewSlide 41Learning phonological distinctions: A hypothesisRemaining problems – lateral inhibitionHypothesis applied to conceptual categoriesLocating Functions: The Proximity PrincipleConsequences of the Proximity PrincipleLearning and the Proximity PrincipleTwo aspects of the proximity principleHow to Explain the Proximity Principle?Proximity: Economic necessityLimits on intercolumn connectivityLocations of available latent connectionsThe role of long-distance fibersTwo Factors in LocalizationGenetically determined proximitySome innate factors relating to localizationInnate factors relating to primary areasA Heterotypical (i.e., genetically built-in) structure Visual motion perceptionA Heterotypical structure: Auditory areas in a cat’s cortexInnate factors relating to localizationApplying the proximity principleImplications of the proximity principleDeriving location from proximity hypothesisSpeech Recognition in the Left HemisphereExercise: Location of Wernicke’s areaAnswer: Location of Wernicke’s areaMore exercisesExperience-based proximityInnate features that support languageSlide 70(1) Learning (2) The proximity principle and “evolutionary learning”Ling 411 – 17Schedule of PresentationsDelclosPlanum TempBanneyerCategoriesRuby TsoWritingBosleySynesthesiaRasmussen2nd languageBrownBilingualismTsaiTonesTu Apr 13 Th Apr 15 Tu Apr 20 Th Apr 22Operations 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 nodesREVIEWOperation of the Networkin 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 levelReviewAdditional 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 satisfiedRequirements that must be assumed(implied by the Hebbian learning principle)Links get stronger when they are successfully used (Hebbian learning)•Learning consists of strengthening them 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 nodesSupport for the abundance hypothesis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 activationLearning – The Basic ProcessLatent nodesLatent linksDedicated nodes and linksLatent nodesLet these links get activatedLearning – The Basic ProcessLearning – The Basic ProcessLatent nodesThen these nodes will get activatedLearning – The Basic ProcessThat will activate these linksLearning – The Basic ProcessThis node gets enough activation to satisfy its thresholdLearning – The Basic ProcessThese links now get strengthened and the node’s threshold gets raised ABThis node is therefore recruitedLearning – The Basic ProcessThis node is now dedicated to function ABABABLearningNext time it gets activated it will send activation on these links to next levelABABLearning: more terms Child nodes Potential Actual Parent nodesABABLearning: 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 levelsLearning in cortical networks:A Darwinian processA trial-and-error process:•Thousands of possibilities availableThe abundance hypothesis•Strengthen those few that succeed“Neural Darwinism” (Edelman)The abundance hypothesis•Needed to allow flexibility of learning•Abundant latent nodesMust be present throughout cortex•Abundant latent connections of


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