Predicting how and when hidden neurons skew measured synaptic interactions

by Braden A. W. Brinkman, Fred Rieke, Eric Shea-Brown, Michael A. Buice

A major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measured neural properties are skewed by interactions between recorded neurons and the “hidden” portion of the network. T o properly interpret neural data and determine how biological structure gives rise to neural circuit function, we thus need a better understanding of the relationships between measured effective neural properties and the true underlying physiological properties. Here, we focus on how the effective s patiotemporal dynamics of the synaptic interactions between neurons are reshaped by coupling to unobserved neurons. We find that the effective interactions from a pre-synaptic neuronr′ to a post-synaptic neuronr can be decomposed into a sum of the true interaction fromr′ tor plus corrections from every directed path fromr′ tor through unobserved neurons. Importantly, the resulting formula reveals when the hidden units have —or do not have—major effects on reshaping the interactions among observed neurons. As a particular example of interest, we derive a formula for the impact of hidden units in random networks with “strong” coupling—connection weights that scale with 1 / N, whereN is the network size, precisely the scaling observed in recent experiments. With this quantitative relationship between me...

Source: PLoS Computational Biology - October 22, 2018 Category: Biology Authors: Braden A. W. Brinkman Source Type: research