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Learning

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

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  1. Learning Computational Cognitive Neuroscience Randall O’Reilly

  2. Overview of Learning • Biology: synaptic plasticity • Computation: • Self organizing – soaking up statistics • Error-driven – getting the right answers

  3. Synapses Change Strength(in response to patterns of activity)

  4. What Changes??

  5. Gettin’ AMPA’d

  6. Opening the NMDA receptor calcium channel Glutamate Na+ CA2+ AMPAr NMDAr Mg2+

  7. Which Way?

  8. Causal Way?

  9. Let’s Get Real..

  10. Urakubo et al, 2008 Model • Highly detailed combination of 3 existing strongly-validated models:

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

  12. What About Real Spike Trains?

  13. Extended Spike Trains =Emergent Simplicity S = 100Hz S = 50Hz S = 20Hz r=.894 dW = f(send * recv) = (spike rate * duration)

  14. XCAL = Linearized BCM • Bienenstock, Cooper & Munro (1982) – BCM: • adaptive thresholdΘ • Lower when less active • Higher when more.. (homeostatic)

  15. Threshold Does Adapt

  16. 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.

  17. Floating Threshold = Long Term Average Activity (Self Org)

  18. 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!)

  19. Limitations of Self-Organizing • Can’t learn to solve challenging problems – driven by statistics, not error..

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

  21. Fast Threshold Adaptation:Late Trains Early Essence of Err-Driven: dW = outcome - expectation

  22. Backpropagation

  23. Where Does Error Come From?

  24. Leabra

  25. Hebbian Learns Correlations

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