Robust online estimation of biophysical neural circuits

TL;DR

This paper investigates robust online methods for estimating parameters in biophysical neural circuits, emphasizing decentralization and redundancy to improve performance under model uncertainty.

Contribution

It introduces a decentralized adaptive observer approach inspired by biological systems to enhance robustness in neural circuit estimation.

Findings

Decentralization improves estimation robustness.

Redundancy helps recover performance under model mismatch.

The approach outperforms centralized algorithms in uncertain conditions.

Abstract

The control of neuronal networks, whether biological or neuromorphic, relies on tools for estimating parameters in the presence of model uncertainty. In this work, we explore the robustness of adaptive observers for neuronal estimation. Inspired by biology, we show that decentralization and redundancy help recover the performance of a centralized recursive mean square algorithm in the presence of uncertainty and mismatch on the internal dynamics of the model.