dana ballard - university of rochester1 distributed synchrony: a model for cortical communication...
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Dana Ballard - University of RochesterDana Ballard - University of Rochester 11
Distributed Synchrony: a model for cortical communication
Madhur Ambastha
Jonathan Shaw
Zuohua Zhang
Dana H. Ballard
Department of Computer Science
University of Rochester
Rochester, NY
Summary
1. There is a computational hierarchy.1. There is a computational hierarchy.
2. At the bottom of the hierarchy is the need to 2. At the bottom of the hierarchy is the need to calibrate. calibrate.
3 . To communicate throughout cortex quickly, 3 . To communicate throughout cortex quickly, calibration uses the calibration uses the band band
Context Select a set of active behaviors
~10s
Resource Map active behaviors onto
motor system ~.3s
Routines update state information ~100ms
Calibration represent sensory/motor/reward
~20ms
Computational quanta
~2ms
1. Computational Timescales
Code Input I with synapses U and output r
Coding cost of residual error
Coding Cost of model
Min E(U,r)= |I-Ur|2 + F(r) + G(U)
Synapses are Trained with Natural Images
Δ r ∝ −
∂ E
∂ r
Δ U ∝ −
∂ E
∂ U
1. Apply Image
2. Change firing
3. Change Synapses
Summary 1:Distributed Synchrony is motivated by four
principle constraints
1. Fast, reliable intercortical communication1. Fast, reliable intercortical communication
2. The ‘need’ for a cell to multiplex2. The ‘need’ for a cell to multiplex
3. Need to poll the input3. Need to poll the input
4 .The need to reproduce observed cell responses4 .The need to reproduce observed cell responses
Summary 2:Isolating Computations = The Binding problem
Solutions:Solutions:
1. There is no binding problem - 1. There is no binding problem - Movshon
2. Fast weight changes at synapses - 2. Fast weight changes at synapses - von der Malsburg
3 .Synchrony encodes the stimulus - 3 .Synchrony encodes the stimulus - Singer
4 .Synchrony encodes the answer - 4 .Synchrony encodes the answer - Koch and others 5 .Synchrony encodes the 5 .Synchrony encodes the processprocess - - Distributed Synchrony
MaxM P(M|D)= MaxM[P(D|M)P(M)/P(D)]
Minimum Description Length - Bayesian Version
Can neglect P(D) and take logs…
MaxM[log P(D|M)+ log P(M)]
Or equivalently minimize negative logs…
MinM[ - log P(D|M) - log P(M)]
If we use exponentiated probability distributions,log cancels negated exponent so…
Coding cost of residual error
Coding cost of model
Cortical Inhibitory CellsCan Oscillate at 20-50 Hz
Beierlein, Gibson, Connors Nature Neuroscience 3 p904 2000
Reconstructionas a function ofCoding Cost
low
high
input feedback error
LGN ON
LGN OFF
LGN ON
LGN OFF