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

TL;DR

This paper introduces a novel, differentiable detection model for identifying precise spiking motifs in neural data, leveraging a generative model and supervised learning, outperforming traditional rate-based methods in synthetic data.

Contribution

The paper presents a new detection approach based on generative model inversion and supervised learning, enabling accurate identification of spiking motifs in neural activity.

Findings

Successfully detects synthetic spiking motifs

Highlights advantages over traditional firing rate codes

Demonstrates potential for application to real data

Abstract

Recently, interest has grown in exploring the hypothesis that neural activity conveys information through precise spiking motifs. To investigate this phenomenon, various algorithms have been proposed to detect such motifs in Single Unit Activity (SUA) recorded from populations of neurons. In this study, we present a novel detection model based on the inversion of a generative model of raster plot synthesis. Using this generative model, we derive an optimal detection procedure that takes the form of logistic regression combined with temporal convolution. A key advantage of this model is its differentiability, which allows us to formulate a supervised learning approach using a gradient descent on the binary cross-entropy loss. To assess the model's ability to detect spiking motifs in synthetic data, we first perform numerical evaluations. This analysis highlights the advantages of using spiking motifs over traditional firing rate based population codes. We then successfully demonstrate that our learning method can 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.