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Berkeley COMPSCI 182 - Lecture Notes

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20 Minute QuizHow does activity lead to structural change?Learning and Memory: IntroductionSkill and Fact Learning may involve different mechanismsModels of LearningHebb’s RuleHebb (1949)Hebb’s ruleLTP and Hebb’s RuleChemical realization of Hebb’s ruleCalcium Channels Facilitate LearningLong Term Potentiation (LTP)The Hebb rule is found with long term potentiation (LTP) in the hippocampusSlide 14Slide 15Slide 16Early and late LTPSlide 18Computational Models based on Hebb’s ruleSlide 20WTA: Stimulus ‘at’ is presentedCompetition starts at category levelCompetition resolvesHebbian learning takes placePresenting ‘to’ leads to activation of category node 1Slide 27Slide 28Slide 29Category 1 is established through Hebbian learning as wellConnectionist Model of Word Recognition (Rumelhart and McClelland)Triangle nodes and McCullough-Pitts Neurons?Representing concepts using triangle nodesSlide 34Slide 35Distributed vs Localist Rep’nSlide 37Recruiting connectionsSlide 39Slide 40Slide 42Slide 43Slide 44Slide 45Slide 46Slide 47Hebb’s rule is not sufficientHebb’s rule is insufficientSlide 50Constraints on Connectionist Models5 levels of Neural Theory of LanguageShort term memoryLong term memorySituational MemoryDreaming and Memory20 Minute QuizFor each of the two questions, you can use text, diagrams, bullet points, etc.1) What are the main events in neural firing and transmission?2) Describe the main events in neural development.How does activity lead to structural change?The brain (pre-natal, post-natal, and adult) exhibits a surprising degree of activity dependent tuning and plasticity.To understand the nature and limits of the tuning and plasticity mechanisms we studyHow activity is converted to structural changes (say the ocular dominance column formation) It is centrally important for us to understand these mechanisms to arrive at biological accounts of perceptual, motor, cognitive and language learningBiological Learning is concerned with this topic.Declarative Non-DeclarativeEpisodic Semantic ProceduralMemoryLearning and Memory: Introductionfacts about a situationgeneral facts skillsSkill and Fact Learning may involve different mechanisms•Certain brain injuries involving the hippocampal region of the brain render their victims incapable of learning any new facts or new situations or faces. –But these people can still learn new skills, including relatively abstract skills like solving puzzles. •Fact learning can be single-instance based. Skill learning requires repeated exposure to stimuli.•Implications for Language Learning?Models of LearningHebbian ~ coincidenceRecruitment ~ one trialSupervised ~ correction (backprop)Reinforcement ~ delayed rewardUnsupervised ~ similarityHebb’s RuleThe key idea underlying theories of neural learning go back to the Canadian psychologist Donald Hebb and is called Hebb’s rule. From an information processing perspective, the goal of the system is to increase the strength of the neural connections that are effective.Hebb (1949)“When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased”From: The organization of behavior.Hebb’s ruleEach time that a particular synaptic connection is active, see if the receiving cell also becomes active. If so, the connection contributed to the success (firing) of the receiving cell and should be strengthened. If the receiving cell was not active in this time period, our synapse did not contribute to the success the trend and should be weakened.Hebb’s Rule: neurons that fire together wire togetherLong Term Potentiation (LTP) is the biological basis of Hebb’s RuleCalcium channels are the key mechanismLTP and Hebb’s RulestrengthenweakenChemical realization of Hebb’s ruleIt turns out that there are elegant chemical processes that realize Hebbian learning at two distinct time scales Early Long Term Potentiation (LTP)Late LTPThese provide the temporal and structural bridge from short term electrical activity, through intermediate memory, to long term structural changes.Calcium Channels Facilitate LearningIn addition to the synaptic channels responsible for neural signaling, there are also Calcium-based channels that facilitate learning. As Hebb suggested, when a receiving neuron fires, chemical changes take place at each synapse that was active shortly before the event.Long Term Potentiation (LTP)These changes make each of the winning synapses more potent for an intermediate period, lasting from hours to days (LTP). In addition, repetition of a pattern of successful firing triggers additional chemical changes that lead, in time, to an increase in the number of receptor channels associated with successful synapses - the requisite structural change for long term memory. There are also related processes for weakening synapses and also for strengthening pairs of synapses that are active at about the same time.The Hebb rule is found with long term potentiation (LTP) in the hippocampus1 sec. stimuliAt 100 hzSchafer collateral pathwayPyramidal cellsWith high-frequency stimulationDuring normal low-frequency trans-mission, glutamate interacts with NMDA and non-NMDA (AMPA) and metabotropic receptors.Enhanced Transmitter ReleaseAMPAEarly and late LTP (Kandel, ER, JH Schwartz and TM Jessell (2000) Principles of Neural Science. New York: McGraw-Hill.)A. Experimental setup for demonstrating LTP in the hippocampus. The Schaffer collateral pathway is stimulated to cause a response in pyramidal cells of CA1. B. Comparison of EPSP size in early and late LTP with the early phase evoked by a single train and the late phase by 4 trains of pulses.Computational Models based onHebb’s ruleThe activity-dependent tuning of the developing nervous system, as well as post-natal learning and development, do well by following Hebb’s rule.Explicit Memory in mammals appears to involve LTP in the Hippocampus.Many computational systems for modeling incorporate versions of Hebb’s rule.Winner-Take-All: Units compete to learn, or update their weights. The processing element with the largest output is declared the winner Lateral inhibition of its competitors. Recruitment LearningLearning Triangle NodesLTP in Episodic


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