Polychronous-Groups | Laurent Perrinet

Working Memory in SNNs

Thu, 16 Apr 2026 14:00:00 +0000

Learning Working Memory in Recurrent Spiking Neural Networks Using Heterogeneous Delays

Laurent Perrinet

[2026-04-16]

Contact me @ laurent.perrinet@univ-amu.fr

Hello, I’m Laurent Perrinet from the Institut des Neurosciences de la Timone, a joint AMU / CNRS unit, and during this talk at the CerCO, I will be speaking about working memory, that is storing patterns with duration of the order of seconds, in spiking neural networks. This is a hard problem as spiking neurons have a limited memory of the order of tens of milliseconds. How can one extend this memory to larger durations? Here, I will be presenting a method for building WM in Spiking Neural Networks by using Heterogeneous Delays.

I’d like to thank Antoine for the invitation 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.

Spiking Neural Networks: Leaky Integrate-and-Fire

[Grimaldi et al, 2023, Precise Spiking Motifs]

Spiking Neural Networks: neurobiology

[Mainen & Sejnowski, 1995]

Spiking Neural Networks: neurobiology

[Mainen & Sejnowski, 1995]

Spiking Neural Networks: neurobiology

[Diesmann et al. 1999]

Spiking Neural Networks: neurobiology

[Haimerl et al, 2019]

Spiking Neural Networks: Leaky Integrate-and-Fire

Review on Precise Spiking Motifs.

Spiking Neural Networks: Heterogeneous Delays

Review on Precise Spiking Motifs.

Heterogeneous Delays Spiking Neural Network: HD-SNN

Spiking Neural Network: Polychronization

Izhikevich (2006)

Spiking Neural Network: Polychronization

Izhikevich (2006)

Spiking Neural Network: Polychronization

LP (2026)

Methods : BPTT (snn Torch) - synthetic target

Methods : Weight initialization

$$ I_j(t) = \sum_{i=1}^{N} \bigl ( \sum_{d=1}^{D} \mathbf{W}_{j, i, d} \cdot s_i(t-d) \bigr ) $$ $$ u_j(t) = \beta \cdot u_j(t-1) \cdot (1 - s_j(t-1)) + I_j(t) $$ $$ s_j(t) = \mathbf{H}[u_j(t) \geq \vartheta] $$ $$ \mathbf{W} \mathbf{C} \approx \mathbf{S} $$ $$ w_{j, i, d} = \frac{1}{N \cdot D \cdot p_A \cdot M} \sum_{\mu=1}^{M} \sum_{t=D+1}^{T} s_{j}^{\mu}(t) \cdot s_i^{\mu}(t-d) $$

Results : recall of target

Results : memory retrieval

Results : recall of target with noise

Results : role of parameters

Learning Working Memory in Recurrent Spiking Neural Networks Using Heterogeneous Delays

Laurent Perrinet

Cerco seminar

[2026-04-16]

Contact me @ laurent.perrinet@univ-amu.fr

Working Memory in SNNs

Wed, 15 Apr 2026 09:00:00 +0000

Learning Working Memory in Recurrent Spiking Neural Networks Using Heterogeneous Delays

Laurent Perrinet

Austrian Symposium on AI, Robotics and Vision

[2026-04-15]

Contact me @ laurent.perrinet@univ-amu.fr

Hello, I’m Laurent Perrinet from the Institut des Neurosciences de la Timone, a joint AMU / CNRS unit, and during this talk at this AIROV workshop on Recent Advances in SNNs, I will be speaking about working memory, that is storing patterns with duration of the order of seconds, in spiking neural networks. This is a hard problem as spiking neurons have a limited memory of the order of tens of milliseconds. How can one extend this memory to larger durations? Here, I will be presenting a method for building WM in Spiking Neural Networks by using Heterogeneous Delays.

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.

Polychronization

Methods : BPTT (snn Torch) - synthetic target

Methods : Weight initialization

Results : recall of target

Results : role of parameters

Results : memory retrieval

Learning Working Memory in Recurrent Spiking Neural Networks Using Heterogeneous Delays

Laurent Perrinet

Austrian Symposium on AI, Robotics and Vision

[2026-04-15]

Contact me @ laurent.perrinet@univ-amu.fr

2023-11-07-snufa.md

Tue, 07 Nov 2023 00:00:00 +0000

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

Laurent Perrinet

SNUFA: Spiking Neural networks as Universal Function Approximators

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

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

Laurent Perrinet

SNUFA: Spiking Neural networks as Universal Function Approximators

laurent.perrinet@univ-amu.fr

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

2023-09-27_icann.md

Wed, 27 Sep 2023 00:00:00 +0000

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

Laurent Perrinet

ICANN workshop on Recent Advances in SNNs

laurent.perrinet@univ-amu.fr

^{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

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

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}

Polychronies (2022 / 2025)

Mon, 18 Jul 2022 14:00:00 +0000

THE POSITION HAS BEEN FILLED.

Description

Why do neurons communicate through action potentials, or spikes? An action potential is a binary event —it can occur or not, without further details— and asynchronous, i.e. it can occur at any time. In the living world, neurons almost systematically use this so-called event-based representation, though we do not yet have a clear idea why. A better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the masses of recorded data. It is also an emerging challenge in computer science to allow the efficient exploitation of a new class of sensors and impulse computers, called neuromorphic, which could allow significant gains in computing time and energy consumption —a major societal challenge in the age of the digital economy and of global warming.

The goal of this project is to bring an interdisciplinary perspective on the computational advantage of time series representations for the brain and for information processing machines. In particular, we will formalize mathematically a representation in an assembly of neurons based on a set of patterns of different relative spike times called polychronous groups. This hypothesis is directly inspired by neurobiological observations in the hippocampus, and the innovative aspect is to expand the capabilities of analog representations based on the firing rate by considering a representation based on repetitions of these polychronous groups at precise times of occurrence. This formalization is particularly well suited to neuromorphic computing, and allows for supervised or self-supervised learning of polychronous groups in any event-driven data. By extending this paradigm to a hierarchy, we envision practical applications of this approach in audio, video or neurobiological signal processing. The cross-fertilization of neuroscience and neuromimetic approaches will be instrumental in understanding the typical or pathological development of such spiking neural networks.

grant number AMX-21-RID-025:

" Ce travail a bénéficié d’une aide du gouvernement français au titre de France 2030, dans le cadre de l’Initiative d’Excellence d’Aix-Marseille Université – A*MIDEX, projet numero AMX-21-RID-025 "
" This work received support from the french government under the France 2030 investment plan, as part of the Initiative d’Excellence d’Aix-Marseille Université – A*MIDEX, under grant number AMX-21-RID-025 ”

Latest news

2023-09-11: Start of post-doc position
2023-05-01: Opening of post-doc position (THE POSITION HAS BEEN FILLED!)
2022-12-29: check out our review paper:
Antoine Grimaldi, Amélie Gruel, Camille Besnainou, Jean-Nicolas Jérémie, Jean Martinet, Laurent U Perrinet (2022). Precise spiking motifs in neurobiological and neuromorphic data.
Cite DOI Hal Code URL arXiv
2022-11-28: Pilot project at the INT brainhack
2022-07-18: Le projet Polychronies est lauréat de l’appel à projets « Interdisciplinarité » !
2022-02-27: read our complete proposal.