Online neural connectivity estimation with ensemble stimulation

TL;DR

This paper introduces an efficient, causal method for estimating neural connectivity in large, sparse networks using ensemble stimulation and convex optimization, enabling online inference of millions of connections.

Contribution

It presents a novel noisy group testing approach that improves efficiency and allows online, scalable neural connectivity estimation with minimal assumptions.

Findings

Connectivity can be recovered with tests growing logarithmically with network size.

The method is related to Variational Bayesian inference and compressed sensing.

Feasible for online inference in networks of tens of thousands of neurons.

Abstract

One of the primary goals of systems neuroscience is to relate the structure of neural circuits to their function, yet patterns of connectivity are difficult to establish when recording from large populations in behaving organisms. Many previous approaches have attempted to estimate functional connectivity between neurons using statistical modeling of observational data, but these approaches rely heavily on parametric assumptions and are purely correlational. Recently, however, holographic photostimulation techniques have made it possible to precisely target selected ensembles of neurons, offering the possibility of establishing direct causal links. Here, we propose a method based on noisy group testing that drastically increases the efficiency of this process in sparse networks. By stimulating small ensembles of neurons, we show that it is possible to recover binarized network connectivity with a number of tests that grows only logarithmically with population size under minimal statistical assumptions. Moreover, we prove that our approach, which reduces to an efficiently solvable convex optimization problem, can be related to Variational Bayesian inference on the binary connection weights, and we derive rigorous bounds on the posterior marginals. This allows us to extend our method to the streaming setting, where continuously updated posteriors allow for optional stopping, and we demonstrate the feasibility of inferring connectivity for networks of up to tens of thousands of neurons online. Finally, we show how our work can be theoretically linked to compressed sensing approaches, and compare results for connectivity inference in different settings.