Statistical Data Assimilation: Formulation and Examples from Neurobiology

TL;DR

This paper reviews statistical data assimilation methods and demonstrates their application in neurobiology, specifically in neural circuit analysis and VLSI neuron modeling, highlighting challenges in network-level investigations.

Contribution

It introduces variational approximation algorithms for SDA and applies them to neural data and VLSI neuron models, advancing neurobiological data analysis techniques.

Findings

Successful application of SDA to individual neuron analysis in HVC.

Development of variational algorithms for biophysical neural data.

Insights into functional connectivity in neural circuits.

Abstract

For the Research Topic Data Assimilation and Control: Theory and Applications in Life Sciences we first review the formulation of statistical data assimilation (SDA) and discuss algorithms for exploring variational approximations to the conditional expected values of biophysical aspects of functional neural circuits. Then we report on the application of SDA to (1) the exploration of properties of individual neurons in the HVC nucleus of the avian song system, and (2) characterizing individual neurons formulated as very large scale integration (VLSI) analog circuits with a goal of building functional, biophysically realistic, VLSI representations of functional nervous systems. Networks of neurons pose a substantially greater challenge, and we comment on formulating experiments to probe the properties, especially the functional connectivity, in song command circuits within HVC.