Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics

Author:

Banerjee Amitava12ORCID,Chandra Sarthak34,Ott Edward125

Affiliation:

1. Department of Physics, University of Maryland, College Park, MD 20742

2. Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD 20742

3. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139

4. McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139

5. Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742

Abstract

Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases, the measured time series data may be subject to limitations, including limited duration, low sampling rate, observational noise, and partial nodal state measurement. However, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality, transfer entropy, and, a machine learning-based method. Furthermore, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.

Funder

National Science Foundation

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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