Paper-Conference

Working Memory in Recurrent Spiking Neural Networks With Heterogeneous Synaptic Delays
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
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 visual motion direction”

Our work explores how populations of neurons in the primary visual cortex (V1) of marmoset monkeys encode visual motion direction, with a particular focus on understanding how uncertainty influences this neural decoding process. Key highlights:

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Detection of spiking motifs of arbitrary length in neural activity using bounded synaptic delays
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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!

“Population decoding of visual motion direction in V1 marmoset monkey: effects of uncertainty”

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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
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