DOI: 10.1101/463364Nov 8, 2018Paper

SIMNETS: a computationally efficient and scalable framework for identifying networks of functionally similar neurons

BioRxiv : the Preprint Server for Biology
Jacqueline Bernadette HynesCarlos E Vargas-Irwin

Abstract

We present the Similarity Networks (SIMNETS) algorithm, a computationally efficient and scalable method for identifying groups of functionally related neurons within larger, simultaneously recorded ensembles. Our approach begins by independently measuring the intrinsic relationship between the activity patterns of each neuron across experimental conditions before making comparisons across neurons (instead of directly comparing firing patterns using measures such as correlations in firing rate or synchrony). This procedure estimates the intrinsic geometry of each neuron's output space and allows us to capture the information processing properties of each neuron in a format that is easily compared between neurons. Dimensionality reduction tools are then used to map the neuron population into a low-dimensional space according to the similarity of their information processing properties. The algorithm's computational complexity scales almost linearly with the number of neurons analyzed and requires minimal assumptions about single-unit encoding properties, making SIMNETS especially well-suited for examining large networks of neurons engaged in complex behavior. We validate the ability of our approach to detect functional groupings ...Continue Reading

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