Trial matching: capturing variability with data-constrained spiking neural networks

Published: 21 Sept 2023, Last Modified: 02 Nov 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: neuroscience, spiking networks, data-constrained modeling, electrophysiological recordings, optimal transport, trial variability, RNN, interpretable machine learning
TL;DR: We fit the parameters of a large recurrent spiking model to explain electrophysiological recordings. To capture the trial-to-trial variability in the data, a loss function based on optimal transport is defined.
Abstract: Simultaneous behavioral and electrophysiological recordings call for new methods to reveal the interactions between neural activity and behavior. A milestone would be an interpretable model of the co-variability of spiking activity and behavior across trials. Here, we model a mouse cortical sensory-motor pathway in a tactile detection task reported by licking with a large recurrent spiking neural network (RSNN), fitted to the recordings via gradient-based optimization. We focus specifically on the difficulty to match the trial-to-trial variability in the data. Our solution relies on optimal transport to define a distance between the distributions of generated and recorded trials. The technique is applied to artificial data and neural recordings covering six cortical areas. We find that the resulting RSNN can generate realistic cortical activity and predict jaw movements across the main modes of trial-to-trial variability. Our analysis also identifies an unexpected mode of variability in the data corresponding to task-irrelevant movements of the mouse.
Supplementary Material: pdf
Submission Number: 6795