3 papers
cs.LG2026
Chemical Reaction Networks Learn Better than Spiking Neural Networks
Sophie Jaffard, Ivo F. Sbalzarini
We mathematically prove that chemical reaction networks without hidden layers can solve tasks for which spiking neural networks require hidden layers. Our proof uses the determinis…
math.ST2026
CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration
Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret
The present work aims at proving mathematically that a neural network inspired by biology can learn a classification task thanks to local transformations only. In this purpose, we…
cs.LG2025
Spiking Neural Models for Decision-Making Tasks with Learning
Sophie Jaffard, Giulia Mezzadri, Patricia Reynaud-Bouret +1
In cognition, response times and choices in decision-making tasks are commonly modeled using Drift Diffusion Models (DDMs), which describe the accumulation of evidence for a decisi…