Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning

Understand

This paper is concerned with the modeling and analysis of two of the most commonly used recurrent neural network models (i.e., Hopfield neural network and firing-rate neural network) with dynamic recurrent connections undergoing Hebbian learning rules.

• To capture the synaptic sparsity of neural circuits we propose a low dimensional formulation.
• We then characterize certain key dynamical properties.
• First, we give biologically-inspired forward invariance results.