Emergent tuning heterogeneity in cortical circuits is sensitive to cellular neuronal dynamics

TL;DR

This paper develops an analytical framework for understanding how cellular neuronal dynamics influence the diversity of responses in cortical circuits, providing a basis for mechanistic circuit inference.

Contribution

It introduces a tractable theoretical model for emergent tuning heterogeneity in balanced state networks with diverse postsynaptic dynamics.

Findings

Population mean response is sensitive to circuit details.

The model allows exact calculation of tuning heterogeneity likelihood.

Response diversity can be analytically linked to cellular dynamics.

Abstract

Cortical circuits exhibit high levels of response diversity, even across apparently uniform neuronal populations. While emerging data-driven approaches exploit this heterogeneity to infer effective models of cortical circuit computation (e.g. Genkin et al. Nature 2025), the power of response diversity to enable inference of mechanistic circuit models is largely unexplored. Within the landscape of cortical circuit models, spiking neuron networks in the balanced state naturally exhibit high levels of response and tuning diversity emerging from their internal dynamics. A statistical theory for this emergent tuning heterogeneity, however, has only been formulated for binary spin models (Vreeswijk & Sompolinsky, 2005). Here we present a formulation of feature-tuned balanced state networks that allows for arbitrary and diverse dynamics of postsynaptic currents and variable levels of heterogeneity in cellular excitability but nevertheless is analytically exactly tractable with respect to the emergent tuning curve heterogeneity. Using this framework, we present a case study demonstrating that, for a wide range of parameters even the population mean response is non-universal and sensitive to mechanistic circuit details. As our theory enables exactly and analytically obtaining the likelihood-function of tuning heterogeneity given circuit parameters, we argue that it forms a powerful and rigorous basis for neural circuit inference.