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Modeling and characterizing stochastic neurons based on in vitro voltage-dependent spike probability functions

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Abstract

Neurons in the nervous system are submitted to distinct sources of noise, such as ionic-channel and synaptic noise, which introduces variability in their responses to repeated presentations of identical stimuli. This motivates the use of stochastic models to describe neuronal behavior. In this work, we characterize an intrinsically stochastic neuron model based on a voltage-dependent spike probability function. We determine the effect of the intrinsic noise in single neurons by measuring the spike time reliability and study the stochastic resonance phenomenon. The model was able to show increased reliability for non-zero intrinsic noise values, according to what is known from the literature, and the addition of intrinsic stochasticity in it enhanced the region in which stochastic-resonance is present. We proceeded to the study at the network level where we investigated the behavior of a random network composed of stochastic neurons. In this case, the addition of an extra dimension, represented by the intrinsic noise, revealed dynamic states of the system that could not be found otherwise. Finally, we propose a method to estimate the spike probability curve from in vitro electrophysiological data.

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Data Availability Statement

This manuscript has associated data in a data repository.

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Acknowledgements

This article was produced as part of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP. It was also supported partially by the S. Paulo Research Foundation (FAPESP) Research, Innovation and Dissemination Center for Neuromathematics (CEPID NeuroMat, Grant No. 2013/07699-0). The authors also thank FAPESP support through Grants nos. 2013/25667-8 (R.F.O.P.), 2015/50122-0 (A.C.R.), 2016/03855-5 (N.L.K.), 2017/07688-9 (R.O.S), 2017/18977-1 (F.S.B), 2018/20277-0 (A.C.R.), and 2019/14962-5 (R.P). V.L. and C.C.C. were supported by a CAPES PhD scholarship. A.C.R. thanks financial support from the National Council of Scientific and Technological Development (CNPq), Grant No. 306251/2014-0. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001.

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Authors and Affiliations

  1. Department of Physics, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil

    Vinicius Lima, Rodrigo F. O. Pena, Renan O. Shimoura, Nilton L. Kamiji, Cesar C. Ceballos & Antonio C. Roque

  2. Institut de Neurosciences des Systèmes (INS, UMR 1106) Université de Aix-Marseille, Marseille, France

    Vinicius Lima

  3. Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, NJ, USA

    Rodrigo F. O. Pena

  4. Vollum Institute, Oregon Health & Science University, Portland, OR, USA

    Cesar C. Ceballos

  5. Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Bernardo do Campo, SP, Brazil

    Fernando S. Borges & Guilherme S. V. Higa

  6. State University of New York Downstate Health Sciences University, New York, NY, USA

    Fernando S. Borges

  7. Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil

    Roberto De Pasquale

Authors
  1. Vinicius Lima
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  2. Rodrigo F. O. Pena
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  6. Fernando S. Borges
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  9. Antonio C. Roque
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Contributions

VL, RFOP, ROS, NLK, CCC, and ACR: conceived the work; VL, RFOP, ROS, CCC, and NLK: work on model implementation, simulation, and theoretical analysis VL, RFOP, ROS, NLK, CCC, and ACR: wrote the manuscript; FSB, GSVH, RP, collected and provided the data used in the study. All authors read, reviewed, and agreed to the published version of the manuscript.

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Correspondence to Vinicius Lima.

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Lima, V., Pena, R.F.O., Shimoura, R.O. et al. Modeling and characterizing stochastic neurons based on in vitro voltage-dependent spike probability functions. Eur. Phys. J. Spec. Top. 230, 2963–2972 (2021). https://doi.org/10.1140/epjs/s11734-021-00160-7

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