Percept formation from neural populations in sensory decision-making tasks

TL;DR

This paper introduces a statistical method to analyze neural population data, revealing how sensory information is integrated over time and across neurons during perceptual decision-making.

Contribution

It presents a novel approach to infer perceptual integration parameters from neural activity, linking covariance structures to percept formation in sensory tasks.

Findings

Method accurately recovers integration window and neuron count from simulated data.

Provides theoretical analysis connecting convergence laws to neural population structure.

First comprehensive interpretation of feedforward percept formation from neural populations.

Abstract

We study a standard linear readout model of perceptual integration from a population of sensory neurons. We show that the readout can be associated to a set of characteristic equations which summarize the joint trial-to-trial covariance structure of neural activities and animal percept. These characteristic equations implicitly determine the readout parameters that were used by the animal to create its percept. In particular, they implicitly constrain the temporal integration window w and the typical number of neurons K which give rise to the percept. Comparing neural and behavioral sensitivity alone cannot disentangle these two sources of perceptual integration, so the characteristic equations also involve a measure of choice signals, like those assessed by the classic experimental measure of choice probabilities. We then propose a statistical method of analysis which allows to recover the typical scales of integration w and K from finite numbers of recorded neurons and recording trials, and show the efficiency of this method on an artificial encoding network. We also study the statistical method theoretically, and relate its laws of convergence to the underlying structure of neural activity in the population, as described through its singular value decomposition. Altogether, our method provides the first thorough interpretation of feedforward percept formation from a population of sensory neurons. It can readily be applied to experimental recordings in classic sensory decision-making tasks, and hopefully provide new insights into the nature of perceptual integration.