Search Results for author: Thomas Mesnard

Found 10 papers, 2 papers with code

Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks

no code implementations15 Nov 2019 Thomas Mesnard, Gaetan Vignoud, Joao Sacramento, Walter Senn, Yoshua Bengio

This reduced system combines the essential elements to have a working biologically abstracted analogue of backpropagation with a simple formulation and proofs of the associated results.

Generalization of Equilibrium Propagation to Vector Field Dynamics

3 code implementations14 Aug 2018 Benjamin Scellier, Anirudh Goyal, Jonathan Binas, Thomas Mesnard, Yoshua Bengio

The biological plausibility of the backpropagation algorithm has long been doubted by neuroscientists.

Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity

no code implementations9 Dec 2016 Thomas Mesnard, Wulfram Gerstner, Johanni Brea

In machine learning, error back-propagation in multi-layer neural networks (deep learning) has been impressively successful in supervised and reinforcement learning tasks.

General Classification

STDP as presynaptic activity times rate of change of postsynaptic activity

no code implementations19 Sep 2015 Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, Yuhuai Wu

We introduce a weight update formula that is expressed only in terms of firing rates and their derivatives and that results in changes consistent with those associated with spike-timing dependent plasticity (STDP) rules and biological observations, even though the explicit timing of spikes is not needed.

Towards Biologically Plausible Deep Learning

no code implementations14 Feb 2015 Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, Zhouhan Lin

Neuroscientists have long criticised deep learning algorithms as incompatible with current knowledge of neurobiology.

Denoising Representation Learning

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