Spike Train decoding Summary Decoding of stimulus from response –Two choice case Discrimination ROC curves –Population decoding MAP and ML estimators

Spike Train decoding



Summary

• Decoding of stimulus from response– Two choice case

• Discrimination• ROC curves

– Population decoding• MAP and ML estimators• Bias and variance• Fisher information, Cramer-Rao bound

– Spike train decoding

Chapter 4

Entropy

Entropy

Mutual information

H_noise< H

Mutual information

KL divergence

Continuous variables

Entropy maximization

Entropy maximization

Population of neurons

Retinal Ganglion Cell Receptive Fields





Temporal processing in LGN



Temporal vs spatial coding

Entropy of spike trains




• Spike train mutual information measurements quantify stimulus specific aspects of neural encoding.

• Mutual information of bullfrog peripheral auditory neurons was estimated– 1.4 bits/sec for broadband noise stimulus– 7.8 bits/sec for bullfrog call-like stimulus

Summary

• Information theory quantifies how much a response says about a stimulus– Stimulus, response entropy– Noise entropy– Mutual information, KL divergence

• Maximizing information transfer yields biological receptive fields– Factorial codes– Equalization– Whitening

• Spike train mutual information

Documents

Spike Train decoding Summary Decoding of stimulus from response –Two choice case Discrimination ROC curves –Population decoding MAP and ML estimators