A shotgun sampling solution for the common input problem in neural connectivity inference

TL;DR

This paper introduces a 'shotgun' sampling approach combined with Bayesian inference to accurately determine neural connectivity despite the common input problem, even with limited simultaneous recordings.

Contribution

It proposes a novel experimental design and scalable Bayesian methods to infer large neural networks from partial observations, overcoming biases caused by common input effects.

Findings

Shotgun sampling eliminates common input bias in network inference.

Accurate estimation of networks with thousands of neurons from partial data.

Incorporating prior information improves inference performance.

Abstract

Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience. The `common input' problem presents the major roadblock: it is difficult to reliably distinguish causal connections between pairs of observed neurons from correlations induced by common input from unobserved neurons. Since available recording techniques allow us to sample from only a small fraction of large networks simultaneously with sufficient temporal resolution, naive connectivity estimators that neglect these common input effects are highly biased. This work proposes a `shotgun' experimental design, in which we observe multiple sub-networks briefly, in a serial manner. Thus, while the full network cannot be observed simultaneously at any given time, we may be able to observe most of it during the entire experiment. Using a generalized linear model for a spiking recurrent neural network, we develop scalable approximate Bayesian methods to perform network inference given this type of data, in which only a small fraction of the network is observed in each time bin. We demonstrate in simulation that, using this method: (1) The shotgun experimental design can eliminate the biases induced by common input effects. (2) Networks with thousands of neurons, in which only a small fraction of the neurons is observed in each time bin, could be quickly and accurately estimated. (3) Performance can be improved if we exploit prior information about the probability of having a connection between two neurons, its dependence on neuronal cell types (e.g., Dale's law), or its dependence on the distance between neurons.