Distributed online estimation of biophysical neural networks

TL;DR

This paper introduces a distributed adaptive observer inspired by neural learning, enabling efficient decentralized estimation of neural network states and parameters with exponential convergence, relevant for biological and neuromorphic systems.

Contribution

It presents a novel decentralized learning-based observer design that reduces observer complexity while ensuring exponential convergence in neural network models.

Findings

Reduces the number of observer states needed for convergence.

Ensures exponential convergence of parameter estimates.

Applicable to biological, biomedical, and neuromorphic systems.

Abstract

In this work, we propose a distributed adaptive observer for a class of nonlinear networked systems inspired by biophysical neural network models. Neural systems learn by adjusting intrinsic and synaptic weights in a distributed fashion, with neuronal membrane voltages carrying information from neighbouring neurons in the network. We show that this learning principle can be used to design an adaptive observer based on a decentralized learning rule that greatly reduces the number of observer states required for exponential convergence of parameter estimates. This novel design is relevant for biological, biomedical and neuromorphic applications.