Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons

TL;DR

This paper demonstrates that randomly connected neural networks can produce emergent selectivity and correlation patterns observed in large neural populations during decision-making tasks, without requiring specific anatomical connectivity.

Contribution

It introduces a computational model showing that random connectivity alone can explain neural selectivity and correlation patterns seen in experimental data, challenging the need for specialized connectivity.

Findings

Random networks generate single-cell selectivity.

Patterns of pairwise correlations match experimental data.

No anatomically defined choice-specific sub-populations are necessary.

Abstract

Advances in neural recording methods enable sampling from populations of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to the theoretical models of computations underlying performance. In the context of decision making in rodents, patterns of functional connectivity between choice-selective cortical neurons, as well as broadly distributed choice information in both excitatory and inhibitory populations, were recently reported [1]. The straightforward interpretation of these data suggests a mechanism relying on specific patterns of anatomical connectivity to achieve selective pools of inhibitory as well as excitatory neurons. We investigate an alternative mechanism for the emergence of these experimental observations using a computational approach. We find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and indistinguishable excitatory and inhibitory readout weight distributions, as observed in recorded neural populations. Further, we make the readily verifiable experimental predictions that, for this type of evidence accumulation task, there are no anatomically defined sub-populations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. This work suggests that distributed stimulus selectivity and patterns of functional organization in population codes could be emergent properties of randomly connected networks.