162 citations · 460 across the 16 of their papers we have counts for
16 papers
Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data
Ariane Delrocq, Wu S. Zihan, Guillaume Bellec +1
The brain learns abstract representations of high-dimensional sensory input, but the plasticity rules that enable such learning are unknown. We study biologically plausible algorit…
Can Local Learning Match Self-Supervised Backpropagation?
Wu S. Zihan, Ariane Delrocq, Wulfram Gerstner +1
While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local self-supervised learning (local-…
Conductance-dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing
Christoph Weilenmann, Hanglin He, Marko Mladenović +6
Modern computers perform pre-defined operations using static memory components, whereas biological systems learn through inherently dynamic, time-dependent processes in synapses an…
Spiking Music: Audio Compression with Event Based Auto-encoders
Martim Lisboa, Guillaume Bellec
Neurons in the brain communicate information via punctual events called spikes. The timing of spikes is thought to carry rich information, but it is not clear how to leverage this…
High-performance deep spiking neural networks with 0.3 spikes per neuron
Ana Stanojevic, Stanisław Woźniak, Guillaume Bellec +3
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 (S…
Trial matching: capturing variability with data-constrained spiking neural networks
Christos Sourmpis, Carl Petersen, Wulfram Gerstner +1
Simultaneous behavioral and electrophysiological recordings call for new methods to reveal the interactions between neural activity and behavior. A milestone would be an interpreta…
Context selectivity with dynamic availability enables lifelong continual learning
Martin Barry, Wulfram Gerstner, Guillaume Bellec
"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…
An Exact Mapping From ReLU Networks to Spiking Neural Networks
Ana Stanojevic, Stanisław Woźniak, Guillaume Bellec +3
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 t…
Mesoscopic modeling of hidden spiking neurons
Shuqi Wang, Valentin Schmutz, Guillaume Bellec +1
Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neur…
Fitting summary statistics of neural data with a differentiable spiking network simulator
Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi +2
Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose par…
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Bernd Illing, Jean Ventura, Guillaume Bellec +1
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…
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
Guillaume Bellec, Franz Scherr, Elias Hajek +3
The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery. This lack o…
Long short-term memory and learning-to-learn in networks of spiking neurons
Guillaume Bellec, Darjan Salaj, Anand Subramoney +2
Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and learning capabilities of the brain. But computing and learning capabilities of RSNN models have…
Deep Rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass +1
Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning ru…
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
Sebastian Schmitt, Johann Klaehn, Guillaume Bellec +22
Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energ…
Pattern representation and recognition with accelerated analog neuromorphic systems
Mihai A. Petrovici, Sebastian Schmitt, Johann Klähn +33
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit partic…