Linking Neural Manifolds to Circuit Structure in Recurrent Networks

Abstract

The classic view of cortical circuits composed of precisely tuned neurons hardly accounts for large-scale recordings indicating that neuronal populations are heterogeneous and exhibit activity patterns evolving on low-dimensional manifolds. Using a modelling approach, we connect these two contrasting views. Our recurrent spiking network models explicitly link the circuit structure with the low-dimensional dynamics of the population activity. Importantly, we show that different circuit models can lead to equivalent low-dimensional dynamics. Nevertheless, we design a method for retrieving the circuit structure from large-scale recordings and test it on simulated data. Our approach not only unifies cortical circuit models with established models of collective neuronal dynamics, but also paves the way for identifying elements of circuit structure from large-scale experimental recordings.