17 citations · 29 across the 10 of their papers we have counts for
6 papers · 1 filter
EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations
Erwann Martin, Maxence Ernoult, Jérémie Laydevant +4
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge.…
Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias
Axel Laborieux, Maxence Ernoult, Benjamin Scellier +3
Equilibrium Propagation (EP) is a biologically-inspired algorithm for convergent RNNs with a local learning rule that comes with strong theoretical guarantees. The parameter update…
Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation
Maxence Ernoult, Julie Grollier, Damien Querlioz +2
Equilibrium Propagation (EP) is a biologically inspired alternative algorithm to backpropagation (BP) for training neural networks. It applies to RNNs fed by a static input x that…
Equilibrium Propagation with Continual Weight Updates
Maxence Ernoult, Julie Grollier, Damien Querlioz +2
Equilibrium Propagation (EP) is a learning algorithm that bridges Machine Learning and Neuroscience, by computing gradients closely matching those of Backpropagation Through Time (…
Synaptic Metaplasticity in Binarized Neural Networks
Axel Laborieux, Maxence Ernoult, Tifenn Hirtzlin +1
While deep neural networks have surpassed human performance in multiple situations, they are prone to catastrophic forgetting: upon training a new task, they rapidly forget previou…
Binding events through the mutual synchronization of spintronic nano-neurons
Miguel Romera, Philippe Talatchian, Sumito Tsunegi +9
The brain naturally binds events from different sources in unique concepts. It is hypothesized that this process occurs through the transient mutual synchronization of neurons loca…