Task Learning through Stimulation-Induced Plasticity in Neural Networks

Abstract

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 nonlinear dynamics. We present a self-contained procedure which, through appropriate spatiotemporal 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 nonlinear 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 computation based on real neurons. Published by the American Physical Society 2024