Efficient Coding Approach Towards Non-Linear Spectro-Temporal Receptive Fields

TL;DR

This paper applies the efficient coding principle to linear non-linear models of auditory spectro-temporal receptive fields, optimizing mutual information and matching physiological data.

Contribution

It introduces an efficient coding framework for non-linear spectro-temporal receptive fields in auditory cortex, extending previous visual and linear auditory models.

Findings

Efficient coding predicts receptive field properties aligning with physiological data.

Optimization of mutual information under metabolic constraints improves model accuracy.

The approach accounts for noise in stimuli and spike generation processes.

Abstract

Linear Non-Linear(LN) models are widely used to characterize the receptive fields of early-stage auditory processing. We apply the principle of efficient coding to the LN model of Spectro-Temporal Receptive Fields(STRFs) of the neurons in primary auditory cortex. The Efficient Coding Principle has been previously used to understand early visual receptive fields and linear STRFs in auditory processing. Efficient coding is realized by jointly optimizing the mutual information between stimuli and neural responses subjected to the metabolic cost of firing spikes. We compare the predictions of the efficient coding principle with the physiological observations, which match qualitatively under realistic conditions of noise in stimuli and the spike generation process.