Guillaume Emmanuel Fernand Bellec

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For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically rea ...

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances ...

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to r ...

Guillaume Emmanuel Fernand Bellec

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Spike frequency adaptation supports network computations on temporally dispersed information

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

An exact mapping from ReLU networks to spiking neural networks

High-performance deep spiking neural networks with 0.3 spikes per neuron

Context selectivity with dynamic availability enables lifelong continual learning

Fast learning without synaptic plasticity in spiking neural networks

An exact mapping from ReLU networks to spiking neural networks' [Neural Networks(vol168, pg 74, 2023)