neuronalX
Inria research scientist & Reservoir activist, bio-inspired AI, computational neuroscience, cognitive robotics
INRIA / IMN / LaBRIBordeaux, France
Pinned Repositories
canary-vocal-sensorimotor-model
Vocal learning model with RNN Decoder and Low-dimensional GAN Generator
EchoRob
Recurrent Neural Network for Syntax Learning with Flexible Representations for Robotic Architectures
low-dimensional-canary-GAN
Synthesize raw canary syllables with generative adversarial networks
Oger
Oger is a toolbox mainly used for Reservoir Computing neural networks, such as Echo State Networks (ESN) and Liquid State Machines (LSM)
Reservoir-Jupyter
Jupyter notebooks to explain Reservoir Computing
reservoirpy
A simple and flexible code for Reservoir Computing architectures like Echo State Networks
awesome-reservoir-computing
Awesome tutorials, papers, projects and tools for Reservoir Computing techniques like Echo State Networks (ESN).
reservoirpy
A simple and flexible code for Reservoir Computing architectures like Echo State Networks
reservoirR
Experimental R interface for ReservoirPy
ESN-WM
A robust model of gated graded working memory
neuronalX's Repositories
neuronalX/birdsong
neuronalX/BiologicallyPlausibleLearningRNN
neuronalX/coglabdx.github.io
Site web du coglab bordelais.
neuronalX/DataSurvey
Datas from lucid dream survey
neuronalX/esn-lm
Training neural language models based on Echo State Networks with different readout functions.
neuronalX/homepage
Homepage at http://www.labri.fr/perso/nrougier/
neuronalX/ip_in_esn
This project is the result of a 1-month internship in the Mnemosyne french research team (Inria lab). My objective was to study echo state networks and try to add intrinsic plasticity (a bio-inspired non-supervised learning method).
neuronalX/jekyll-slack
Generate a static archive from a Slack data export
neuronalX/la-perche-a-hormones
La Perche à Hormone (jeu vidéo) : aidez les poissons à ne pas chagner de sexe en dépolluant les rivières !
neuronalX/part_of_speech_tagger
neuronalX/twostagelearning
Code and data for paper on efficient two-stage learning in songbirds and beyond