Paper-Conference

Introduction to Art & Science
Invited talk (~50 minutes, followed by ~10 minutes of questions) at NeuroDays 2026: The Art of Neuroscience, Marseille, France, …
2026-11-26
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Modelling memory to understand the brain
Modelling working memory —the ability to store and recall precise temporal patterns— remains an open challenge in …
2026-11-09 Computational Neuroscience, NeuroAI & Machine Learning
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Modelling memory to understand the brain
A New Look for Convolutional Deep Networks
By construction, deep convolutional networks treat all pixels equally. Result: they are energy-hungry, fragile to rotations or zooms, …
2026-11-03 Behavioural Neuroscience, Biological Neuroscience, Computer Vision, NeuroAI & Machine Learning, Visual Neuroscience
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Bio-plausible statistical regularizer for spiking LeJEPA
Joint Embedding Predictive Architectures (JEPA) offer a promising path toward self-supervised learning, but their application to SNNs …
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Modelling working memory for neuroscience
Modelling working memory —the ability to store and recall precise temporal patterns— remains an open challenge in …
2026-09-18
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Modelling working memory for neuroscience
Working Memory in Recurrent Spiking Neural Networks With Heterogeneous Synaptic Delays
The code and results at the time of the submission of this camera-ready paper is accessible in this commit see a related presentation: …
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Efficient Working Memory in a recurrent Spiking Neural Network
Working memory — the ability to store and recall precise temporal patterns — remains an open challenge for spiking neural …
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Optimal Transport Theory to extract Spiking Motifs
Whether cortical neurons encode information through firing rates or precise spike timing remains a central question in neuroscience. …
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Working Memory in Recurrent Spiking Neural Networks Using Heterogeneous Synaptic Delays
Working memory, the ability to store and retrieve precise temporal patterns of neural activity, remains a fundamental challenge for …
2026-04-16 Computational Neuroscience, Computer Vision, NeuroAI & Machine Learning
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Learning Working Memory in Recurrent Spiking Neural Networks Using Heterogeneous Delays
See the accompanying code: https://github.com/SpikeAI/MNESIS The code and results at the time of the presentation is accessible in …
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Physiological state Monitoring: a Riemannian Geometry based-model
This study investigates the use of Riemannian geometry to detect and monitor physiological states such as mental workload (MWL) from an …
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Population decoding of visual motion direction
🧠 Excited to share our latest research led by Alexandre Lainé and presented this summer at AREADNE 2026! “Population decoding of …
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Detection of spiking motifs of arbitrary length in neural activity using bounded synaptic delays
follow-up paper: Thomas Kronland-Martinet, Stéphane Viollet, Laurent U Perrinet (2025). Detection of spiking motifs of arbitrary …
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Robust Unsupervised Learning of Spike Patterns with Optimal Transport Theory
Temporal sequences are an important feature of neural information processing in biology. Neurons can fire a spike with millisecond …
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Robust Unsupervised Learning of Spike Patterns with Optimal Transport Theory
Classification of Mental Workload Spatial Effects using Riemannian Manifold
This study investigates the use of Riemannian geometry to classify mental workload from an EEG dataset collected in an aeronautical …
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Classification of Mental Workload Spatial Effects using Riemannian Manifold
Integrating the What and Where Visual Pathways to Improve CNN Categorisation
Convolutional Neural Networks (CNNs) have been widely used for categorisation tasks over the past decades. Many studies have attempted …
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Integrating the What and Where Visual Pathways to Improve CNN Categorisation
Population decoding of visual motion direction in V1 marmoset monkey : effects of uncertainty
🧠 Excited to share our latest research led by Alexandre Lainé and presented this summer at CNS2025 in beautiful Firenze, Italy! …
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Population decoding of visual motion direction in V1 marmoset monkey : effects of uncertainty
An open-source vision-science tool for the auto-regressive generation of dynamic stochastic textures Motion Clouds
Motion Clouds are a generative model for naturalistic visual stimulation that offer full parametric control and more naturalism than …
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Diverse Neuronal Responses to Visual Precision in Cat Cortical Area 21a: Unraveling the Complexity of Orientation Processing
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Retinotopy in CNN's implements Efficient Visual Search
Read the corresponding paper Jean-Nicolas Jérémie, Emmanuel Daucé, Laurent U Perrinet (2026). Foveated Retinotopy Improves …
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Retinotopy in CNN's implements Efficient Visual Search
Retinotopy in CNN's implements Efficient Visual Search
While foveated vision, a trait shared by many animals including humans, is a major contributor to biological visual performance, it has …
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Retinotopy in CNN's implements Efficient Visual Search