Predicting how and when hidden neurons skew measured synaptic interactions

Abstract

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. To 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 spatiotemporal dynamics of the synaptic interactions between neurons are reshaped by coupling to unobserved neurons. We find that the effective interactions from a pre-synaptic neuron $r^{\prime }$ to a post-synaptic neuron $r$ can be decomposed into a sum of the true interaction from $r^{\prime }$ to $r$ plus corrections from every directed path from $r^{\prime }$ to $r$ 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 $1/\sqrt{N}$, where $N$ is the network size, precisely the scaling observed in recent experiments. With this quantitative relationship between measured and true interactions, we can study how network properties shape effective interactions, which properties are relevant for neural computations, and how to manipulate effective interactions.

~14 pages + 6 figures (main text), ~18 pages + 3 figures (Methods), ~22 pages + 3 figures (supporting info). v3 is a significantly expanded version of the original upload

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• State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics
• Phase transitions in in vivo or in vitro populations of spiking neurons belong to different universality classes
• Applications of information geometry to spiking neural network behavior