Unsupervised functional neurocartography of the Aplysia buccal ganglion

Abstract

A major limitation of large-scale neuronal recordings is the difficulty in locating homologous neurons in different subjects, referred to as the "correspondence" issue. This issue stems, at least in part, from the lack of a unique feature that unequivocally identifies each neuron. One promising approach to this problem is the functional neurocartography framework developed by Frady et al. (2016), in which neurons are identified by a semisupervised machine learning algorithm using a combination of multiple selected features. Here, the framework was adapted to the buccal ganglia of Aplysia. Multiple features were derived from neuronal activity during motor pattern generation, responses to peripheral nerve stimulation, and the spatial properties of each cell. The feature set was optimized based on its potential usefulness in discriminating neurons from each other, and then used to match putatively homologous neurons across subjects with the functional neurocartography software. An alternative matching method based on a cyclic matching algorithm was also developed, which allows for unsupervised extraction of groups of neurons and automated selection of high-quality matches. This improvement enabled unsupervised implementation of the machine learning algorithm, thereby enhancing scalability of the analysis. This study paves the way for investigating the roles of both well-characterized and previously uncharacterized neurons in Aplysia, and helps to adapt the neurocartography framework to other systems.