Accurate Detection of Spiking Motifs in Neurobiological Data by Learning Heterogeneous Delays of a Spiking Neural Network

Abstract

Recently, there has been an increase in interest in exploring the hypothesis that neural activity conveys information through precise spiking motifs. To investigate this phenomenon, several algorithms have been proposed to detect such motifs in Single Unit Activity recorded from populations of neurons. Based on the inversion of a generative model of raster plot synthesis, we present a novel detection model. This model derives an optimal detection procedure in the form of logistic regression combined with temporal convolution. Its differentiability allows for a supervised learning approach using gradient descent on the binary cross-entropy loss. To assess the model’s ability to detect spiking motifs in synthetic data, numerical evaluations are performed. This analysis emphasizes the benefits of utilizing spiking motifs instead of traditional firing rate-based population codes. Our learning method was able to successfully recover synthetically generated spiking motifs, indicating its potential for further applications. In the future, we aim to extend this method to real neurobiological data, where the ground truth is unknown, to explore and detect spiking motifs in a more natural and biologically relevant context.

Laurent U Perrinet

Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.