Open-Science | 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

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Mon, 15 Dec 2025 00:00:00 +0000

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DynTex: A Real-Time Generative Model of Dynamic Naturalistic Luminance Textures

Mon, 01 Sep 2025 00:00:00 +0000

🚀 Excited to share our new paper:

“DynTex: A real-time generative model of dynamic naturalistic luminance textures”

…now published in Journal of Vision!

🔹 Why it matters: Dynamic textures (e.g., fire, water, foliage) are everywhere, but modeling them in real-time has been a challenge. DynTex bridges this gap with a biologically inspired, efficient approach.

🔹 Key innovation: A generative model that captures the spatiotemporal statistics of natural scenes while running in real-time.

🔹 Applications: Computer vision, neuroscience, VR/AR, and more.📖

Read it here: https://doi.org/10.1167/jov.25.11.2

More on: https://laurentperrinet.github.io/publication/meso-25/

#DynamicTextures #ComputationalNeuroscience #ComputerVision #GenerativeModels #OpenScience

linkedin, mastodon, bluesky
The Motion Clouds stimuli were originally presented in the following paper (page links to other sresources)
Paula Sanz Leon, Ivo Vanzetta, Guillaume S Masson, Laurent U Perrinet (2012). Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception. Journal of Neurophysiology.
PDF Cite DOI HAL URL arXiv Supp
examples of use: https://laurentperrinet.github.io/sciblog/categories/motionclouds.html

2018-03-26 : PhD Program: course in Computational Neuroscience

Mon, 26 Mar 2018 00:00:00 +0000

PhD Program: course in Computational Neuroscience

Context

Computational neuroscience is an expending field that is proving to be essential in neurosciences. The aim of this course will be to provide a common solid background in computational neurosciences. The course will comprise historical recall of the field and a description of the different modelling approaches that are currently developed, including details about their specificities, limits and advantages.

Objective

The course aims at introducing students with the major tools that will be necessary during their thesis to model or analyze their neuroscientific results. While it will start by a short, generic introduction, we will then explore different systems at different scales. On the first day, we will study the different possible regimes in which a single neuron can behave, while progressively introducing the theory of dynamical systems to understand these more globally. Then, during the second day, we will introduce methods to analyze neuroscientific data in general, such as Bayesian methods and information theory. This will be implemented by simple practical examples.

Language of intervention

English

Number of hours

~20 hours (session 1=7 + session 2=7 + session 3=4)

Max participants

15 for the practical sessions (afternoon Day 2 and Day 3), unlimited for theoretical courses

Public priority

PhD students

Public concerned

PhD students, interested M2 students and postdocs

Location

Institut des Neurosciences de la Timone (INT)

Keywords

neuronal modelling, neural circuit modelling, information theory, decoding and encoding

Targets

Understanding how computational modelling can be used to formulate and solve neuroscience problems at different spatial and temporal scales; learning the formal notions of information, encoding and decoding and experimenting their use on toy datasets

Program

First session: Introduction to modeling single neurons (morning); An introduction to neural masses: modeling assemblies of neurons up to capturing collective oscillations and resting state dynamics in a mean-field model - presentation of the Virtual Brain software (afternoon) - Second session: An overview on “What is encoding?” “What is decoding?”: formalization of the notion of information in neural activity; shared and transferred information; integration, segregation and complexity (morning). Bayesian probabilities, the Free-energy principle and Active Inference, with practical demonstrations in python (afternoon). Third session: the problem of information estimation in practice. Practical exercices in Matlab: estimating entropy and stimulus decodability from spike trains; comparing coding hypotheses (morning).

Pre-required

Basic knowledge of statistics and probability and calculus (differential equations,…) is useful, but steps will be explained and complex math avoided as much as possible. Practical exercises are in python and/or MATLAB, so basic knowledge of these environments is a plus.

program

day 1 : 2018-03-26 : an introduction to Computational Neuroscience

09:30-12:30 = Introduction to modeling single neurons (LaP)
14:00-17:00 = An introduction to neural masses: modeling assemblies of neurons up to capturing resting state dynamics in a mean-field model - presentation of the Virtual Brain software (DaB)

day 2 : 2018-03-27 : Information theory / bayesian models

09:15-10:30 = An overview on “What is encoding?” “What is decoding?”: formalization of the notion of information in neural activity (DaB)
11:00-12:15 = (…continued after the coffee break: ) Live information! From sharing information to transferring information (and a glimpse into the zoo of higher-order friends) (DaB)
14:00-17:10 = Probabilities, the Free-energy principle and Active Inference (LuP).

day 3 : 2018-03-28 : Practical course on Information theory

09:30-12:30 = Practical course on Information theory (DaB)

More material related to the course

–

day 1 - morning : the single neuron

site du livre de Gerstner et al “Neuronal Dynamics”: http://neuronaldynamics.epfl.ch/
A (longer) introduction to the Hodgkin-Huxley model in three steps by Dr Stefano Luccioli
An interactive course with Wulfram Gerstner https://www.edx.org/course/neuronal-dynamics-computational-epflx-bio465-1x
His book ONLINE http://cn.epfl.ch/~gerstner/NeuronalDynamics-MOOC1.html

day 1 - afternoon : neural mass models

Another interactive course @ Washington University https://www.coursera.org/course/compneuro
Collection of didactic material for the EU FP7 ITN Neural Engineering Transformative Technology http://www.neural-engineering.eu/training/index.html
Didactic material from Lab in Computational Neuroscience http://neuro.fi.isc.cnr.it/index.php?page=didactic-material
A open source simulator of a whole brain which runs on your laptop, “The Virtual Brain”: http://thevirtualbrain.org

day 2 - morning : information theory

The best book on information theory and decoding, freely available directly from the author: http://www.inference.phy.cam.ac.uk/itprnn/book.html
a gentle introduction to bayesian methods : https://homepages.inf.ed.ac.uk/pseries/Peg_files/Chapter9_SotiropoulosSeries.pdf

day 2 - afternoon : bayesian models

an interesting read : http://cognitrn.psych.indiana.edu/busey/q551/PDFs/PredictivCodingRaoBallard.pdf
a tutorial on free-energy : some exercises : http://www.sciencedirect.com/science/article/pii/S0022249615000759
solutions to the tutorial : https://laurentperrinet.github.io/sciblog/posts/2017-01-15-bogacz-2017-a-tutorial-on-free-energy.html

contacts

LaP: Laurent Pezard «Laurent.Pezard@univ-amu.fr»
DaB: Demian Battaglia «demian.battaglia@univ-amu.fr», INS
LuP: Laurent U Perrinet «laurent.perrinet@univ-amu.fr», INT
PhD program: Nicole Malfait «Nicole.Malfait@univ-amu.fr», Anna Montagnini «anna.montagnini@univ-amu.fr»

ANEMO: Quantitative tools for the ANalysis of Eye MOvements

Mon, 01 Jan 2018 00:00:00 +0000

see a write-up in
Chloé Pasturel, Anna Montagnini, Laurent U Perrinet (2020). Humans adapt their anticipatory eye movements to the volatility of visual motion properties. PLoS Computational Biology.
Cite DOI Press Pdf Code URL bioRxiv
as presented at https://eyemovements.sciencesconf.org/
get the poster
code : https://github.com/invibe/ANEMO/

ANR TRAJECTORY (2016/2019)

Wed, 27 Apr 2016 00:00:00 +0000

Global motion processing is a major computational task of biological visual systems. When an object moves across the visual field, the sequence of visited positions is strongly correlated in space and time, forming a trajectory. These correlated images generate a sequence of local activation of the feed-forward stream. Local properties such as position, direction and orientation can be extracted at each time step by a feed-forward cascade of linear filters and static non-linearities. However such local, piecewise, analysis ignores the recent history of motion and faces several difficulties, such as systematic delays, ambiguous information processing (e.g., aperture and correspondence problems61) high sensitivity to noise and segmentation problems when several objects are present. Indeed, two main aspects of visual processing have been largely ignored by the dominant, classical feed-forward scheme. First, natural inputs are often ambiguous, dynamic and non-stationary as, e.g., objects moving along complex trajectories. To process them, the visual system must segment them from the scene, estimate their position and direction over time and predict their future location and velocity. Second, each of these processing steps, from the retina to the highest cortical areas, is implemented by an intricate interplay of feed-forward, feedback and horizontal interactions1. Thus, at each stage, a moving object will not only be processed locally, but also generate a lateral propagation of information. Despite decades of motion processing research, it is still unclear how the early visual system processes motion trajectories. We, among others, have proposed that anisotropic diffusion of motion information in retinotopic maps can contribute resolving many of these difficulties25 13. Under this perspective, motion integration, anticipation and prediction would be jointly achieved through the interactions between feed-forward, lateral and feedback propagations within a common spatial reference frame, the retinotopic maps.

Addressing this question is particularly challenging, as it requires to probe these sequences of events at multiple scales (from individual cells to large networks) and multiple stages (retina, primary visual cortex (V1)). “TRAJECTORY” proposes such an integrated approach. Using state-of-the-art micro- and mesoscopic recording techniques combined with modeling approaches, we aim at dissecting, for the first time, the population responses at two key stages of visual motion encoding: the retina and V1. Preliminary experiments and previous computational studies demonstrate the feasibility of our work. We plan three coordinated physiology and modeling work-packages aimed to explore two crucial early visual stages in order to answer the following questions: How is a translating bar represented and encoded within a hierarchy of visual networks and for which condition does it elicit anticipatory responses? How is visual processing shaped by the recent history of motion along a more or less predictable trajectory? How much processing happens in V1 as opposed to simply reflecting transformations occurring already in the retina?

The project is timely because partners master new tools such as multi-electrode arrays and voltage-sensitive dye imaging for investigating the dynamics of neuronal populations covering a large segment of the motion trajectory, both in retina and V1. Second, it is strategic: motion trajectories are a fundamental aspect of visual processing that is also a technological obstacle in computer vision and neuroprostheses design. Third, this project is unique by proposing to jointly investigate retinal and V1 levels within a single experimental and theoretical framework. Lastly, it is mature being grounded on (i) preliminary data paving the way of the three different aims and (ii) a history of strong interactions between the different groups that have decided to join their efforts.

The Marseille team

Frédéric Chavane (DR, CNRS, NEOPTO team) is working in the field of vision research for about 20 years with a special interest in the role of lateral interactions in the integration of sensory input in the primary visual cortex. His recent work suggest that lateral interactions mediated by horizontal intracortical connectivity participates actively in the input normalization that controls a wide range of function, from the contrast-response gain to the representation of illusory or real motion. His expertise range from microscopic (intracellular recordings) to mesoscopic (optical imaging, multi-electrode array) recording scales in the primary visual cortex of anesthetized and awake behaving animals.
Laurent Perrinet (CR, CNRS, NEOPTO team). His scientific interests focus on bridging computational understanding of neural dynamics and low-level sensory processing by focusing on motion perception. He is the author of papers in machine learning, computational neuroscience and behavioral psychology. One key concept is the use of statistical regularities from natural scenes as a main drive to integrate local neural information into a global understanding of the scene. In a recent paper that he coauthored (in Nature Neuroscience), he developed a method to use synthesized stimuli targeted to analyze physiological data in a system-identification approach.
Ivo Vanzetta (CR, CNRS, NEOPTO team). His scientific interests focus on how to optimally use photonics-based imaging methods to investigate visual information processing in low-level visual areas, in the anesthetized and awake animal (rodent & primate). As can be seen from his bibliographic record, these methods include optical imaging of intrinsic signals and voltage sensitive dyes and, recently, 2 photon microscopy. Finally I. Vanzetta has an ongoing collaboration with L. Perrinet on the utilization of well-controlled, synthesized nature-like visual stimuli to probe the response characteristics of the primate’s visual system (Sanz-Leon & al. 2012).

Progress meeting ANR TRAJECTORY

Time January 15th, 2018
Location INT
General presentation of the grant, see Anr TRAJECTORY
Overview of my current projects https://laurentperrinet.github.io/sciblog/files/2017-11-15_ColloqueMaster.html
MotionClouds with trajectories https://laurentperrinet.github.io/sciblog/posts/2018-01-16-testing-more-complex-trajectories.html or https://laurentperrinet.github.io/sciblog/posts/2018-11-13-testing-more-complex-trajectories.html

A predictive sequence is essential in resolving the coherence problem. The sequence in which a set of local motion is shown is essential for the detection of global motion. we replicate here the experiments by Scott Watamaniuk and colleagues. They have shown behaviourally that a dot in noise is much more detectable when it follows a coherent trajectory, up to an order of magnitude of 10 times what would be predicted by the local components of the trajectory. In this movie we observe white noise and at first sight, no information is detectable. In fact, there is a dot moving along some smooth linear trajectory. Since this is compatible with a predictive sequence, it is much easier to see the dot (from left to right in the top of the image, a smooth pursuit helps to catch it). This simple experiment shows that, even if local motion is similar in both movies, a coherent trajectory is more easy to track. Obviously, we may thus conclude that the whole trajectory is more that its individual parts, and that the independence hypothesis does not hold if we want to account for the predictive information in input sequences such as seems to be crucial for the AP.

Acknowledgement

This work was supported by ANR project “TRAJECTORY” N° ANR-15-CE37-0011.

Open Science

Wed, 27 Apr 2016 00:00:00 +0000

To enable the dissemination of the knowledge that is produced in our lab, we share all source code with open source licences. This includes code to reproduce results obtained in papers (e.g. (Perrinet, Adams and Friston, 2015), (Perrinet and Bednar, 2015), (Khoei et, 2017), (Perrinet, 2019), (Pasturel et al, 2020), (Dauce et al, 2020)) or courses and slides (e.g. 2019-04-03: vision and modelization, 2019-04-18_JNLF, …) and also the development of the following libraries on GitHub.

Follow @laurentperrinet

HD natural images database for sparse coding

A dataset of natural images, acquired with a Canon EOS6D and Canon EOS650. It has been curated to facilitate research, namely in sparse coding at the moment, but can be used for future endeavors. Maintainer: Hugo Ladret.

get the dataset
See the preprint publication @
Hugo Ladret, Laurent U Perrinet (2023). Convolutional Sparse Coding is improved by heterogeneous uncertainty modeling. ICLR 2023 SNN Workshop.
PDF Cite URL

Bayesian Change Point

A python implementation of Adams & MacKay 2007 “Bayesian Online Changepoint Detection” for binary inputs in Python.

Source code
See the final publication @
Chloé Pasturel, Anna Montagnini, Laurent U Perrinet (2020). Humans adapt their anticipatory eye movements to the volatility of visual motion properties. PLoS Computational Biology.
Cite DOI Press Pdf Code URL bioRxiv

ANEMO: Quantitative tools for the ANalysis of Eye MOvements

This implementation proposes a set of robust fitting methods for the extraction of eye movements parameters.

Source code
See a poster @ Pasturel, Montagnini and Perrinet (2018)
This library was used in the following publication @
Chloé Pasturel, Anna Montagnini, Laurent U Perrinet (2020). Humans adapt their anticipatory eye movements to the volatility of visual motion properties. PLoS Computational Biology.
Cite DOI Press Pdf Code URL bioRxiv

LeCheapEyeTracker

Work-in-progress : an eye tracker based on webcams.

Source code

Biologically inspired computer vision ( Python)

SLIP: a Simple Library for Image Processing

This library collects different Image Processing tools for use with the LogGabor and SparseEdges libraries.

LogGabor: a Simple Library for Image Processing

This library defines the set of LogGabor kernels. These are generic edge-like filters at different scales, phases and orientations. The library develops a simple method to construct a simple multi-scale linear transform.

Web-site
Source code
This library is detailed in the following publication
Sylvain Fischer, Filip Šroubek, Laurent U Perrinet, Rafael Redondo, Gabriel Cristóbal (2007). Self-Invertible 2D Log-Gabor Wavelets. International Journal of Computer Vision.
PDF Cite DOI Code URL
LogGabor filters are used in numerous computer vision applications and reaches 177 citations on Google Scholar (last updated 22/10/2021).

SparseEdges: sparse coding of natural images

Our goal here is to build practical algorithms of sparse coding for computer vision.

This class exploits the SLIP and LogGabor libraries to provide with a sparse representation of edges in images.

Web-site
Source code
This algorithm was presented in the following paper, which is available as a reprint
Laurent U Perrinet (2015). Sparse Models for Computer Vision. Biologically Inspired Computer Vision.
Cite DOI Code URL arXiv
It was notably used in the following paper
Laurent U Perrinet, James A Bednar (2015). Edge co-occurrences can account for rapid categorization of natural versus animal images. Scientific Reports.
Cite DOI Code URL HAL

Sparse Hebbian Learning : unsupervised learning of natural images

This is a collection of python scripts to test learning strategies to efficiently code natural image patches. This is here restricted to the framework of the SparseNet algorithm from Bruno Olshausen (http://redwood.berkeley.edu/bruno/sparsenet/).

Source code
This algorithm was presented in the following paper
Laurent U Perrinet (2010). Role of homeostasis in learning sparse representations. Neural Computation.
PDF Cite DOI Hal Code URL arXiv
54 citations on Google Scholar (last updated 22/10/2021)
Follow-up paper
Laurent U Perrinet (2019). An adaptive homeostatic algorithm for the unsupervised learning of visual features. Vision.
PDF Cite DOI Code URL

MotionClouds

MotionClouds are random dynamic stimuli optimized to study motion perception.

Web-site
Source code using Python.
This algorithm was presented in the following paper
Paula Sanz Leon, Ivo Vanzetta, Guillaume S Masson, Laurent U Perrinet (2012). Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception. Journal of Neurophysiology.
PDF Cite DOI HAL URL arXiv Supp
3746 citations on Google Scholar (last updated 04/09/2025)
examples of use: https://laurentperrinet.github.io/sciblog/categories/motionclouds.html
Follow-up paper
Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation.
PDF Cite DOI URL arXiv

Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation.
PDF Cite DOI URL arXiv
This library was notably used in the following papers:
Claudio Simoncini, Laurent U Perrinet, Anna Montagnini, Pascal Mamassian, Guillaume S Masson (2012). More is not always better: dissociation between perception and action explained by adaptive gain control. Nature Neuroscience.
PDF Cite DOI URL

Cesar U Ravello, Laurent U Perrinet, Maria-José Escobar, Adrián G Palacios (2019). Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli. Scientific Reports.
Cite DOI Press URL HAL

Hugo Ladret, Nelson Cortes, Lamyae Ikan, Frédéric Chavane, Christian Casanova, Laurent U Perrinet (2023). Cortical recurrence supports resilience to sensory variance in the primary visual cortex. Nature Communications Biology.
PDF Cite DOI Dataset Code URL bioRxiv HAL

PyNN

PyNN is a simulator-independent language for building neuronal network models using Python.

Web-site
Source code
This algorithm was presented in the following paper
Andrew P Davison, Daniel Bruderle, Jochen Eppler, Jens Kremkow, Eilif Muller, Dejan Pecevski, Laurent U Perrinet, Pierre Yger (2008). PyNN: A Common Interface for Neuronal Network Simulators. Frontiers in Neuroinformatics.
PDF Cite Project DOI URL HAL
619 citations on Google Scholar (last updated 22/10/2021)

MotionClouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception

Thu, 22 Mar 2012 14:00:00 +0000

NeuralEnsemble: Towards a meta-environment for network modeling and data analysis

Thu, 01 Jan 2009 00:00:00 +0000

see a follow-up:
Andrew P Davison, Daniel Bruderle, Jochen Eppler, Jens Kremkow, Eilif Muller, Dejan Pecevski, Laurent U Perrinet, Pierre Yger (2008). PyNN: A Common Interface for Neuronal Network Simulators. Frontiers in Neuroinformatics.
PDF Cite Project DOI URL HAL

PyNN: A Common Interface for Neuronal Network Simulators

Tue, 01 Jan 2008 00:00:00 +0000

PyNN is a simulator-independent language for building neuronal network models using Python.

Web-site
Source code
619 citations on Google Scholar (last updated 22/10/2021)

PyNN: towards a universal neural simulator API in Python

Mon, 01 Jan 2007 00:00:00 +0000

see a follow-up:
Andrew P Davison, Daniel Bruderle, Jochen Eppler, Jens Kremkow, Eilif Muller, Dejan Pecevski, Laurent U Perrinet, Pierre Yger (2008). PyNN: A Common Interface for Neuronal Network Simulators. Frontiers in Neuroinformatics.
PDF Cite Project DOI URL HAL