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DOIRON, BRENT D (contact); SMITH, MATTHEW A; YU, BYRON M Neuronal population dynamics within and across cortical areas EB026953
A major goal of theoretical neuroscience is to develop mechanistic network models that can reproduce key aspects of neuronal activity recorded in the brain. There are two key parts of fitting a network model to neuronal recordings: 1) incisive measures to compare neuronal recordings with the activity produced by network models, and 2) automatic methods to efficiently fit network model parameters to data. We propose a systematic framework using population activity statistics based on dimensionality reduction and a Bayesian optimization algorithm to efficiently fit model parameters to data. The proposed population statistics go beyond the commonly-used single-neuron and pairwise statistics and raise the bar for comparing models to data. The Bayesian optimization algorithm efficiently fit the parameters using fewer iterations than brute force methods. We emphasize limits of model capacity where a given model reproduces some, but not all, of the desired features of neuronal recordings. We used our algorithm to study which aspects of neuronal activity recorded in macaque V4 can be reproduced by classical balanced networks (CBN) and spatial balanced networks (SBN). We found that SBN has better capacity compared to CBNs in fitting V4 data and discovered interesting trade-offs between different types of activity statistics, thereby revealing limits of model capacity. These insights can be used to guide the development of future network models whose activity resembles neuronal recordings even more closely.