Inference of C. elegans neural network structure from calcium flurescence time series data with reservoir computing

Calcium imaging is a popular technique to record neural activity from live, freely-moving animals, like C. elegans or mice. Inference of neural network connections from short duration, low temporal resolution time series data of calcium flurescence from individual neurons or neuron groups is important in understanding how neurons interact together to orchestrate behavior or stimulus response in animals. Previous works in this direction have mostly focused on inferring such neural interactions by measuring correlations and information flow between calcium flurescence time series of different neurons, without having an understanding of the underlying neural dynamics. In this work, we train a particular kind of machine learning architechture, called "reservoir computing", on publicly available whole-brain calcium imaging time series datasets from C. elegans. The trained reservoir is able to learn a model of the C. elegans neural dynamics, which we further use to estimate the connectivity structure between the neurons (this is based on our previous works, reported in [1], [2]). We validate our results against the known C. elegans neural connectome, and show that our neural connectivity inference method performs better than transfer entropy-based method, a commonly used technique for network inference from biological data. These results indicate that data-driven and machine learning-based modeling of neural dynamics has the potentiality to outperform traditional network inference techniques.

References:

[1] A. Banerjee, J. Pathak, R. Roy, J.G. Restrepo, E. Ott, CHAOS 29, 121104 (2019).

[2] A. Banerjee, J.D. Hart, R. Roy, E.Ott, Physical Review X 11 (3), 031014 (2021).