Systematic errors in connectivity inferred from activity in strongly coupled recurrent circuits

Abstract

Abstract Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is slow, expensive, or often infeasible to directly measure the wiring diagrams of neural microcircuits, there has long been an interest in estimating them from neural recordings. We show that even sophisticated inference algorithms, applied to large volumes of data from every node in the circuit, are biased toward inferring connections between unconnected but strongly correlated neurons, a situation that is common in strongly recurrent circuits. This e ect, representing a failure to fully “explain away” non-existent connections when correlations are strong, occurs when there is a mismatch between the true network dynamics and the generative model assumed for inference, an inevitable situation when we model the real world. Thus, effective connectivity estimates should be treated with especial caution in strongly connected networks when attempting to infer the mechanistic basis of circuit activity. Finally, we show that activity states of networks injected with strong noise or grossly perturbed away from equilibrium may be a promising way to alleviate the problems of bias error.