The synaptic Basis for Learning and Memory: a Theoretical approach

Transcription

1 Theoretical Neuroscience II: Learning, Perception and Cognition The synaptic Basis for Learning and Memory: a Theoretical approach Harel Shouval Phone: harel.shouval@uth.tmc.edu Course web page: Strong claim: Synaptic plasticity is the only game in town. Weak Claim: Synaptic plasticity is a game in town.

2 Different examples of learning and memory: Learning to see/hear etc. unsupervised learning. Learning not to stick your hand in the electricity reinforcement learning This class supervised learning Learning to separate different types of objects - classification Remembering the face of your teacher episodic memory

3 The cortex has ~10 9 neurons. Each Neuron has up to 10 4 synapses

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5 Central Hypothesis Changes in synapses underlie the basis of learning, memory and some aspects of development. What is the connection between these seemingly very different phenomena? Do we have experimental evidence for this hypothesis A cellular correlate of Learning, memoryreceptive field plasticity

6 Classical Conditioning Ear Hebb s rule A Nose B Tongue When an axon in cell A is near enough to excite cell B and repeatedly and persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A s efficacy in firing B is increased D. O. Hebb (1949)

7 Two examples of Machine learning based on synaptic plasticity 1.The Perceptron (Rosenblatt 1962) 2. Associative memory

8 THE PERCEPTRON in 2D (classification) Example in 2D (on board): Actual output: -w 0 w 1 w 2 x 2 µ O µ = "(w 1 x 1 µ + w 2 x 2 µ # w 0 ) where $ "(x) = % 1 x > 0 & 0 x # 0 µ is the pattern label Desired output: y µ 1 "(x) 0 x Learning = changing weights (w s) to obtain y µ = O µ

9 THE PERCEPTRONin N-D: (Classification) & Threshold unit: O µ = "(# w i i x µ i $ w 0 ) where "(x) = ' 1 x > 0 ( 0 x % 0 x µ where is the output for input pattern, are the synaptic weights. w i w 1 w 2 w 3 w 4 w 5

10 AND x1 x2 y Linearly seprable

11 OR x1 x2 y Linearly separable

12 Perceptron learning rule: w 1 w 2 w 3 w 4 w 5

13 Famous images Associative memory: Names Albert Input desired output Marilyn Harel 1. Feed forward matrix networks 2. Attractor networks

14 Associative memory: Hetero associative A α Auto associative A A B β B B Hetero associative

15 Why did I show you these examples? These are examples in which changes in synaptic weights are the basis for learning (Perceptron) and memory (Associative memory).

16 Synaptic plasticity evoked artificially Examples of Long term potentiation (LTP) and long term depression (LTD). LTP First demonstrated by Bliss and Lomo in Since then induced in many different ways, usually in slice. LTD, robustly shown by Dudek and Bear in 1992, in Hippocampal slice.

17 Artificially induced synaptic plasticity. Presynaptic rate-based induction Bear et. al. 94

18 Depolarization based induction Feldman, 2000

19 Spike timing dependent plasticity Markram et. al. 1997

20 At this level we know much about the cellular and molecular basis of synaptic plasticity. But how do we know that synaptic plasticity as observed on the cellular level has any connection to learning and memory? What types of criterions can we use to answer this question?

21 Assessment criterions for the synaptic hypothesis: (From Martin and Morris 2002) 1. DETECTABILITY: If an animal displays memory of some previous experience (or has learnt a new task), a change in synaptic efficacy should be detectable somewhere in its nervous system. 2. MIMICRY: If it were possible to induce the appropriate pattern of synaptic weight changes artificially, the animal should display apparent memory for some past experience which did not in practice occur.

22 3. ANTEROGRADE ALTERATION: Interventions that prevent the induction of synaptic weight changes during a learning experience should impair the animal s memory of that experience (or prevent the learning). 4. RETROGRADE ALTERATION: Interventions that alter the spatial distribution of synaptic weight changes induced by a prior learning experience (see detectability) should alter the animals memory of that experience (or alter the learning).

23 Detectability Example from Rioult-Pedotti

24 Example: Inhibitory avoidance Fast Depends on Hippocampus Whitlock et. al. 2006

25 Occlusion of LTP in trained hemisphere More LTD in trained hemisphere (Riolt-Pedoti 2000)

26 Mimicry: Generate a false memory, teach a skill by directly altering the synaptic connections. This is the ultimate test, and at this point in time it is science fiction.

27 ANTEROGRADE ALTERATION: Interventions that prevent the induction of synaptic weight changes during a learning experience should impair the animal s memory of that experience (or prevent the learning). This is the most common approach. It relies on utilizing the known properties of synaptic plasticity as induced artificially.

28 Example: Spatial learning is impaired by block of NMDA receptors (Morris, 1989) Morris water maze rat platform

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30 4. RETROGRADE ALTERATION: Interventions that alter the spatial distribution of synaptic weight changes induced by a prior learning experience should alter the animals memory of that experience (or alter the learning). Lacuna TM

31 Receptive field plasticity is a cellular correlate of learning. What is a receptive field? First described somatosensory receptive fields (Mountcastle) Best known example visual receptive fields

32 Summary End of Short introduction- continue if have time