# Distributed online estimation of biophysical neural networks

arXiv: https://arxiv.org/abs/2204.01472 · HTML: https://mlsanity.com/paper/2204.01472/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2022.

## Built on by (most cited first, 2 of 2)

- [Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology](https://mlsanity.com/paper/2305.15385/) (2023)
- [Fast reconstruction of degenerate populations of conductance-based neuron models from spike times](https://mlsanity.com/paper/2509.12783/) (2025)

## Abstract (arXiv)

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.