  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.
