Linking Neural Manifolds to Circuit Structure in Recurrent Networks

Abstract

AbstractWhile analyses of large-scale neural recording indicate that the activity of heterogeneous populations of neurons follows collective dynamics on low-dimensional neural manifolds, it has remained challenging to reconcile this picture with the classical view of precisely tuned neurons interacting with each other in an ordered circuit structure. Using a modelling approach, we connect these two contrasting views. First, we propose a theoretical framework that explicitly links the circuit structure and the emergent low-dimensional dynamics of the population activity in models of recurrent neural networks. The theory predicts a non-unique relationship between the two, which we illustrate with concrete examples. We then propose a method for retrieving the circuit structure from recordings of the population activity and test it on artificial data. Our approach provides not only a unifying framework for circuit and field models on one side, and low-rank networks on the other side, but also opens the perspective to identify principles of circuit structure from large-scale recordings.