Shaping the distribution of neural responses with interneurons in a recurrent circuit model

TL;DR

This paper introduces a computational model demonstrating how interneurons in recurrent circuits can dynamically shape neural response distributions to optimize information encoding, linking circuit properties to response transformations.

Contribution

It presents a normative model based on optimal transport that connects interneuron connectivity and activation functions to the distribution of neural responses in efficient coding.

Findings

Circuit learns nonlinear transformations mapping natural images to Gaussian responses

Interneuron adjustments significantly reduce neural response dependencies

Model provides a framework for controlling neural response distributions

Abstract

Efficient coding theory posits that sensory circuits transform natural signals into neural representations that maximize information transmission subject to resource constraints. Local interneurons are thought to play an important role in these transformations, dynamically shaping patterns of local circuit activity to facilitate and direct information flow. However, the relationship between these coordinated, nonlinear, circuit-level transformations and the properties of interneurons (e.g., connectivity, activation functions, response dynamics) remains unknown. Here, we propose a normative computational model that establishes such a relationship. Our model is derived from an optimal transport objective that conceptualizes the circuit's input-response function as transforming the inputs to achieve an efficient target response distribution. The circuit, which is comprised of primary neurons that are recurrently connected to a set of local interneurons, continuously optimizes this objective by dynamically adjusting both the synaptic connections between neurons as well as the interneuron activation functions. In an example application motivated by redundancy reduction, we construct a circuit that learns a dynamical nonlinear transformation that maps natural image data to a spherical Gaussian, significantly reducing statistical dependencies in neural responses. Overall, our results provide a framework in which the distribution of circuit responses is systematically and nonlinearly controlled by adjustment of interneuron connectivity and activation functions.