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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??.

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Learning
Learning

Computational Cognitive Neuroscience

Randall O’Reilly


Overview of learning
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
Synapses Change Strength(in response to patterns of activity)



Gettin ampa d
Gettin’ AMPA’d


Opening the nmda receptor calcium channel
Opening the NMDA receptor calcium channel

Glutamate

Na+

CA2+

AMPAr

NMDAr

Mg2+





Urakubo et al 2008 model
Urakubo et al, 2008 Model

  • Highly detailed combination of 3 existing strongly-validated models:


Allosteric nmda captures stdp including higher order and time integration effects
Allosteric” NMDA Captures STDP(including higher-order and time integration effects)



Extended spike trains emergent simplicity
Extended Spike Trains =Emergent Simplicity

S = 100Hz

S = 50Hz

S = 20Hz

r=.894

dW = f(send * recv) =

(spike rate * duration)


Xcal linearized bcm
XCAL = Linearized BCM

  • Bienenstock, Cooper & Munro (1982) – BCM:

  • adaptive thresholdΘ

    • Lower when less active

    • Higher when more..

      (homeostatic)



Computational self organizing and error driven
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.



Self organizing learning
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!)


Limitations of self organizing
Limitations of Self-Organizing

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



Fast threshold adaptation late trains early
Fast Threshold Adaptation: (Error-Driven)Late Trains Early

Essence of Err-Driven: dW = outcome - expectation


Backpropagation
Backpropagation (Error-Driven)



Leabra
Leabra (Error-Driven)


Hebbian learns correlations
Hebbian (Error-Driven) Learns Correlations


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