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A Measure of Concurrent Neural Firing Activity Based on Mutual Information

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Abstract

Multiple methods have been developed in an attempt to quantify stimulus-induced neural coordination and to understand internal coordination of neuronal responses by examining the synchronization phenomena in neural discharge patterns. In this work we propose a novel approach to estimate the degree of concomitant firing between two neural units, based on a modified form of mutual information (MI) applied to a two-state representation of the firing activity. The binary profile of each single unit unfolds its discharge activity in time by decomposition into the state of neural quiescence/low activity and state of moderate firing/bursting. Then, the MI computed between the two binary streams is normalized by their minimum entropy and is taken as positive or negative depending on the prevalence of identical or opposite concomitant states. The resulting measure, denoted as Concurrent Firing Index based on MI (CFIMI), relies on a single input parameter and is otherwise assumption-free and symmetric. Exhaustive validation was carried out through controlled experiments in three simulation scenarios, showing that CFIMI is independent on firing rate and recording duration, and is sensitive to correlated and anti-correlated firing patterns. Its ability to detect non-correlated activity was assessed using ad-hoc surrogate data. Moreover, the evaluation of CFIMI on experimental recordings of spiking activity in retinal ganglion cells brought insights into the changes of neural synchrony over time. The proposed measure offers a novel perspective on the estimation of neural synchrony, providing information on the co-occurrence of firing states in the two analyzed trains over longer temporal scales compared to existing measures.

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Acknowledgments

This research has been supported by the Ministry of Education, Science and Technological Development through the project no. 451-03-68/2020-14/200156: “Innovative scientific and artistic research from the FTS (activity) domain” and from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement number 856967. Luca Faes acknowledges funding from Ministero dell’Istruzione, dell’Università e della Ricerca—PRIN 2017 (PRJ-0167), “Stochastic forecasting in complex systems”. The authors express gratitude to Leonardo Ricci and Alessio Perinelli for sharing Matlab code for JODI method, and to Danica Despotovic for useful discussion.

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Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

    Gorana Mijatovic & Tatjana Loncar-Turukalo

  2. Department of Neurosurgery, Stanford University, Stanford, CA, United States

    Nebojsa Bozanic

  3. Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK

    Nina Milosavljevic & Riccardo Storchi

  4. Department of Engineering, University of Palermo, Palermo, Italy

    Luca Faes

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  1. Gorana Mijatovic
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Correspondence to Gorana Mijatovic.

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Mijatovic, G., Loncar-Turukalo, T., Bozanic, N. et al. A Measure of Concurrent Neural Firing Activity Based on Mutual Information. Neuroinform 19, 719–735 (2021). https://doi.org/10.1007/s12021-021-09515-w

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