Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons

TL;DR

This paper presents a new method to distinguish intrinsic neuronal interactions from extrinsic stimulus-driven correlations in sensory neural networks, enabling more accurate modeling of neural responses.

Contribution

The authors introduce a novel approach to separately infer intrinsic couplings and stimulus encoding, improving the understanding of neural interactions regardless of stimulus variations.

Findings

Couplings inferred are stimulus-independent across different stimuli.

The model accurately predicts collective neural responses.

Effective separation of intrinsic and extrinsic correlations demonstrated on retinal data.

Abstract

Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer {population models of interacting neurons that collectively encode stimulus information}. The key to disentangling intrinsic from extrinsic correlations is to infer the {couplings between neurons} separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus.