  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.
