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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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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)

What changes

What Changes??

Gettin ampa d

Gettin’ AMPA’d

Opening the nmda receptor calcium channel

Opening the NMDA receptor calcium channel







Which way

Which Way?

Causal way

Causal Way?

Let s get real

Let’s Get Real..

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)

What about real spike trains

What About Real Spike Trains?

Extended spike trains emergent simplicity

Extended Spike Trains =Emergent Simplicity

S = 100Hz

S = 50Hz

S = 20Hz


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..


Threshold does adapt

Threshold Does Adapt

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.

Floating threshold long term average activity self org

Floating Threshold = Long Term Average Activity (Self Org)

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..

Floating threshold medium term synaptic activity error driven

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

Fast threshold adaptation late trains early

Fast Threshold Adaptation:Late Trains Early

Essence of Err-Driven: dW = outcome - expectation



Where does error come from

Where Does Error Come From?



Hebbian learns correlations

Hebbian Learns Correlations

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