Basile Confavreux

Inhgrams for engrams: co-active inhibitory-inhibitory plasticity shapes inhibitory assemblies that stabilize and recall embedded engrams through disinhibition

This paper introduces a computational model showing that inhibitory-inhibitory plasticity enables stable recall of overlapping memory engrams through disinhibition.

Learning to play with spikes: characterizing, predicting, and engineering unsupervised plasticity rules for spiking reservoir computing

This paper characterizes and predicts effective unsupervised plasticity rules for spiking reservoir computing by analyzing their performance on a Pong game task.

Memory by a thousand rules: Automated discovery of multi-type plasticity rules reveals variety and degeneracy at the heart of learning.

This study uses automated search algorithms to discover diverse synaptic plasticity rules that support memory tasks and network stability.

Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.

This paper uses evolutionary strategies to meta-learn plasticity rules in spiking networks, finding stable rules for synapse types but facing challenges with complex co-active rules.

Indistinguishable network dynamics can emerge from unalike plasticity rules

The paper shows that multiple distinct plasticity rules can produce similar network dynamics, highlighting the need to consider degenerate mechanisms in plasticity studies.

A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network

This paper uses meta-learning to discover synaptic plasticity rules that can produce desired neural functions, re-discovering known rules and finding new ones for complex tasks.