reading population codes: a neural implementation of ideal observers
Post on 23-Jan-2016
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Reading population codes: a neural implementation of ideal observers
Sophie Deneve, Peter Latham, and Alexandre Pouget
Stimulus (s) neurons
encode
Response (r)
decode
Tuning curves
• sensory and motor info often encoded in “tuning curves”
• neurons give a characteristic “bell shaped” response
Difficulty of decoding
• noisy neurons create variable responses to same stimuli
• brain must estimate encoded variables from the “noisy hill” of a population response
Population vector estimator
• assign each neuron a vector
• vector length is proportional to activity
• vector direction corresponds to preferred direction
Sum vectors
Population vector estimator
• Vector summation is equivalent to fitting a cosine function
• peak of cosine is estimate of direction
How good is an estimator?
• need to compare variance of estimator after repeated presentations to a lower bound
• the maximum likelihood estimate gives the lower variance bound for a given amount of independent noise
VS
Stimulus (s) neurons
encode
Response (r)
decode
Maximum Likelihood Decoding
Maximum likelihood estimator
Decoding
Encoding
Goal: biological ML estimator
• recurrent neural network with broadly tuned units
• can achieve ML estimate with noise independent of firing rate
• can approximate ML estimate with activity-dependent noise
General Architecture
• units are fully connected and are arranged in frequency columns and orientation rows
• weights implement a 2-D Gaussian filter:
20
20
Preferred Frequency
Preferred orientation
PΘ
Pλ
Input tuning curves
• circular normal functions with some spontaneous activity:
• Gaussian noise is added to inputs:
Unit updates & normalization
• units are convolved with filter (local excitation)
• responses are normalized divisively (global inhibition)
Results
• Rapidly converges
•strongly dependent on contrast
Results
• sigmoidal response curve after 3 iterations, becomes a step after 20
• actual neuron
Noise Effects
• Width of input tuning curve held constant
• width of output tuning curve varied by adjusting spatial extent of the weights
Flat Noise
Proportional Noise
Analysis
Q1: Why does the optimal width depend on noise?
Q2: Why does the network perform better for flat noise?
Flat Noise
Proportional Noise
Analysis
Smallest achievable variance:
= inverse of the covariance matrix of the noise
= vector of the derivative of the input tuning curve with respect to
For Gaussian noise:
Trace term is 0 when R is independent of Θ (flat noise)
Θ
Summary
• network gives a good approximation of the optimal tuning curve determined by ML
• type of noise (flat vs proportional) affected variance and optimal tuning width
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