# Interpretation of Correlated Neural Variability from Models of Feed-Forward and Recurrent Circuits

## TL;DR

This paper uses a recurrent neural network model to interpret correlated variability in neural responses, revealing how network parameters influence stimulus discrimination and information processing.

## Contribution

It demonstrates that a recurrent network with random connections can replicate observed neural correlation statistics and links circuit parameters to coding performance.

## Key findings

- Recurrent network model reproduces observed noise and signal correlation relations.
- Network parameters directly affect stimulus discriminability.
- Recurrent connections influence the stimulus dependence of correlations.

## Abstract

The correlated variability in the responses of a neural population to the repeated presentation of a sensory stimulus is a universally observed phenomenon. Such correlations have been studied in much detail, both with respect to their mechanistic origin and to their influence on stimulus discrimination and on the performance of population codes. In particular, recurrent neural network models have been used to understand the origin (or lack) of correlations in neural activity. Here, we apply a model of recurrently connected stochastic neurons to interpret correlations found in a population of neurons recorded from mouse auditory cortex. We study the consequences of recurrent connections on the stimulus dependence of correlations, and we compare them to those from alternative sources of correlated variability, like correlated gain fluctuations and common input in feed-forward architectures. We find that a recurrent network model with random effective connections reproduces observed statistics, like the relation between noise and signal correlations in the data, in a natural way. In the model, we can analyze directly the relation between network parameters, correlations, and how well pairs of stimuli can be discriminated based on population activity. In this way, we can relate circuit parameters to information processing.