Task learning through stimulation-induced plasticity in neural networks

Synaptic plasticity dynamically shapes the connectivity of neural systems and is key to learning processes in the brain. To what extent the mechanisms of plasticity can be exploited to drive a neural network and make it perform some kind of computational task remains unclear. This question, relevant...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Borra, Francesco, Cocco, Simona, Monasson, Rémi
Format: Article
Language:English
Subjects:
Bioengineering
Learning
Neural networks
Neurons
Nonlinear control
Nonlinear dynamics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Synaptic plasticity dynamically shapes the connectivity of neural systems and is key to learning processes in the brain. To what extent the mechanisms of plasticity can be exploited to drive a neural network and make it perform some kind of computational task remains unclear. This question, relevant in a bioengineering context, can be formulated as a control problem on a high-dimensional system with strongly constrained and non-linear dynamics. We present a self-contained procedure which, through appropriate spatio-temporal stimulations of the neurons, is able to drive rate-based neural networks with arbitrary initial connectivity towards a desired functional state. We illustrate our approach on two different computational tasks: a non-linear association between multiple input stimulations and activity patterns (representing digit images), and the construction of a continuous attractor encoding a collective variable in a neural population. Our work thus provides a proof of principle for emerging paradigms of in vitro computation based on real neurons.
ISSN:2331-8422
DOI:10.48550/arxiv.2412.01683