Real-time tracking of neuronal network structure using data assimilation

Franz Hamilton, Tyrus Berry, Nathalia Peixoto, and Timothy Sauer
Phys. Rev. E 88, 052715 – Published 21 November 2013

Abstract

A nonlinear data assimilation technique is applied to determine and track effective connections between ensembles of cultured spinal cord neurons measured with multielectrode arrays. The method is statistical, depending only on confidence intervals, and requiring no form of arbitrary thresholding. In addition, the method updates connection strengths sequentially, enabling real-time tracking of nonstationary networks. The ensemble Kalman filter is used with a generic spiking neuron model to estimate connection strengths as well as other system parameters to deal with model mismatch. The method is validated on noisy synthetic data from Hodgkin-Huxley model neurons before being used to find network connections in the neural culture recordings.

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  • Received 17 May 2013

DOI:https://doi.org/10.1103/PhysRevE.88.052715

©2013 American Physical Society

Authors & Affiliations

Franz Hamilton1, Tyrus Berry2, Nathalia Peixoto1, and Timothy Sauer2

  • 1Electrical and Computer Engineering, George Mason University, Fairfax, Virginia 22030, USA
  • 2Mathematical Sciences, George Mason University, Fairfax, Virginia 22030, USA

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Issue

Vol. 88, Iss. 5 — November 2013

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