Jean‐Pierre Nadal

The paper introduces a confidence-controlled Hebbian learning rule that efficiently extracts category membership from optimized stimulus encoding layers, outperforming reward-modulated Hebbian learning.

This study shows that an attractor neural network model can reproduce human confidence judgments in perceptual decisions, linking confidence to the nonlinear dynamics of the decision-making process.

This paper proposes a computational framework for cerebellar learning using perturbations and validates it with in vitro experiments, contradicting the current consensus on synaptic plasticity.