| Novel visual computations

$logos$

laurent.perrinet@univ-amu.fr

^{https://laurentperrinet.github.io/talk/2023-11-07-snufa}

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

Detecting spiking motifs using heterogeneous delays: supervised learning

^{https://laurentperrinet.github.io/talk/2023-11-07-snufa}

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

Laurent Perrinet

SNUFA: Spiking Neural networks as Universal Function Approximators

Core Mechanism of Spiking Motif Detection

Core Mechanism of Spiking Motif Detection

Core Mechanism of Spiking Motif Detection

From generating raster plots to inferring spiking motifs

Detecting spiking motifs using heterogeneous delays

Detecting spiking motifs using heterogeneous delays: supervised learning

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

Laurent Perrinet

SNUFA: Spiking Neural networks as Universal Function Approximators