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Learning Computational Cognitive Neuroscience Randall O’Reilly

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Learning. Computational Cognitive Neuroscience Randall O’Reilly. Overview of Learning. Biology: synaptic plasticity Computation: Self organizing – soaking up statistics Error-driven – getting the right answers. Synapses Change Strength (in response to patterns of activity). What Changes??. - PowerPoint PPT Presentation

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Page 1: Learning

Learning

Computational Cognitive NeuroscienceRandall O’Reilly

Page 2: Learning

2

Overview of Learning

Biology: synaptic plasticity Computation:

Self organizing – soaking up statistics Error-driven – getting the right answers

Page 3: Learning

3

Synapses Change Strength(in response to patterns of activity)

Page 4: Learning

4

What Changes??

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Gettin’ AMPA’d

Page 6: Learning

Opening the NMDA receptor calcium channel

Glutamate

NMDArAMPAr

CA2+Na+

Mg2+

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7

Which Way?

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XCAL = Linearized BCM Bienenstock, Cooper &

Munro (1982) – BCM: adaptive threshold Θ

Lower when less active Higher when more.. (homeostatic)

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Threshold Does Adapt

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Computational: Self-Organizing and Error-Driven

Self-organizing = learn general statistics of the world.

Error-driven = learn from difference between expectation and outcome.

Both can be achieved through XCAL.

Page 11: Learning

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Floating Threshold = Long Term Average Activity (Self Org)

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Self Organizing Learning

Inhibitory Competition: only some get to learn Rich get richer: winners detect even better

But also get more selective (hopefully) Homeostasis: keeping things more evenly

distributed (higher taxes for the rich!)

Page 13: Learning

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Limitations of Self-Organizing

Can’t learn to solve challenging problems – driven by statistics, not error..

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Where Does Error Come From?

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Floating Threshold = Medium Term Synaptic Activity (Error-Driven)

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Fast Threshold Adaptation:Late Trains Early

Essence of Err-Driven: dW = outcome - expectation

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Backpropagation: Mathematics of Error-driven Learning

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Biological Derivation of XCAL Curve

Can use a detailed model of Spike Timing Dependent Plasticity (STDP) to derive the XCAL learning curve

Provides a different perspective on STDP..

Page 19: Learning

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Causal Learning?

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Let’s Get Real..

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Urakubo et al, 2008 Model Highly detailed combination of 3 existing

strongly-validated models:

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“Allosteric” NMDA Captures STDP(including higher-order and time integration effects)

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What About Real Spike Trains?

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Extended Spike Trains =Emergent Simplicity

S = 100Hz S = 20HzS = 50Hz

r=.894dW = f(send * recv) = (spike rate * duration)

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Leabra

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Hebbian Learns Correlations


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