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Inferring connectivity in neuronal networks remains a key challenge instatistical neuroscience. The `common input' problem presents the majorroadblock: it is difficult to reliably distinguish causal connections betweenpairs of observed neurons from correlations induced by common input fromunobserved neurons. Since available recording techniques allow us to samplefrom only a small fraction of large networks simultaneously with sufficienttemporal resolution, naive connectivity estimators that neglect these commoninput effects are highly biased. This work proposes a `shotgun' experimentaldesign, in which we observe multiple sub-networks briefly, in a serial manner.Thus, while the full network cannot be observed simultaneously at any giventime, we may be able to observe most of it during the entire experiment. Usinga generalized linear model for a spiking recurrent neural network, we developscalable approximate Bayesian methods to perform network inference given thistype of data, in which only a small fraction of the network is observed in eachtime bin. We demonstrate in simulation that, using this method: (1) The shotgunexperimental design can eliminate the biases induced by common input effects.(2) Networks with thousands of neurons, in which only a small fraction of theneurons is observed in each time bin, could be quickly and accuratelyestimated. (3) Performance can be improved if we exploit prior informationabout the probability of having a connection between two neurons, itsdependence on neuronal cell types (e.g., Dale's law), or its dependence on thedistance between neurons.

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