| Novel visual computations

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

Laurent Perrinet

ICANN workshop on Recent Advances in SNNs

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^{https://laurentperrinet.github.io/talk/2023-09-27-icann}

Hello, I’m Laurent Perrinet from the Institut des Neurosciences de la Timone, a joint AMU / CNRS unit, and during this talk at this ICANN workshop on Recent Advances in SNNs, I’ll be presenting a method for the Accurate Detection of Spiking Motifs by Learning Heterogeneous Delays of a Spiking Neural Network, and how it may also impact the design of SNNs. I’d like to thank Sander Bohté and Sebastian Otte for the organization of this workshop and you for listening. These slides are available from my web-site, along with a number of references. The outline of the talk is as follows: first, I’ll describe how one may perform computations using Heterogeneous Delays - and present a toy model example; then, I’ll show real scale example quantifying the performance on synthetic data ; and finally, I’ll present how this SNN is in fact differentiable and may be extended for future applications.

Core Mechanism of Spiking Motif Detection

From generating raster plots to inferring spiking motifs

A In this work, this principle was framed in a probabilistic setting such that we could provide an optimal scheme for detecting generic spiking motifs which may be superposed at random times. Starting with 10 presynaptic inputs, this model allows to generate a synthetic raster plot as the combination of four different spiking motifs. B These motifs are defined by a positive (red) or negative (blue) contribution to the spiking probability which are represented here. C Applying a Bayesian approach, we may define four formal spiking neurons which will integrate the incoming spiking information from the presynaptic neurons - this analog signal can then be thresholded to give the detection of each spiking motif (vertical) bar which was here always exact with respect to the ground truth (stars). D The beauty of this is that we can recover in the presynaptic raster plot the contribution of each spiking motif to the original raster plot.

Detecting spiking motifs using heterogeneous delays

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

Laurent Perrinet

ICANN workshop on Recent Advances in SNNs

$logos$

laurent.perrinet@univ-amu.fr

^{https://laurentperrinet.github.io/talk/2023-09-27-icann}

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

^{https://laurentperrinet.github.io/talk/2023-09-27-icann}