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A Mathematical Model of Spontaneous Action Potential Based on Stochastics Synaptic Noise Dynamics in Non-Neural Cells

Author

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  • Chitaranjan Mahapatra

    (Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA 94115, USA
    Biomedical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India)

  • Inna Samuilik

    (Institute of Applied Mathematics, Riga Technical University, LV-1048 Riga, Latvia)

Abstract

We developed a mathematical model to simulate the dynamics of background synaptic noise in non-neuronal cells. By employing the stochastic Ornstein–Uhlenbeck process, we represented excitatory synaptic conductance and integrated it into a whole-cell model to generate spontaneous and evoke cellular electrical activities. This single-cell model encompasses numerous biophysically detailed ion channels, depicted by a set of ordinary differential equations in Hodgkin–Huxley and Markov formalisms. Consequently, this approach effectively induced irregular spontaneous depolarizations (SDs) and spontaneous action potentials (sAPs), resembling electrical activity observed in vitro. The input resistance decreased significantly, while the firing rate of spontaneous action potentials increased. Moreover, alterations in the ability to reach the action potential threshold were observed. Background synaptic activity can modify the input/output characteristics of non-neuronal excitatory cells. Hence, suppressing these baseline activities could aid in identifying new pharmaceutical targets for various clinical diseases.

Suggested Citation

  • Chitaranjan Mahapatra & Inna Samuilik, 2024. "A Mathematical Model of Spontaneous Action Potential Based on Stochastics Synaptic Noise Dynamics in Non-Neural Cells," Mathematics, MDPI, vol. 12(8), pages 1-13, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1149-:d:1373866
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    References listed on IDEAS

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    1. Daniele Linaro & Marco Storace & Michele Giugliano, 2011. "Accurate and Fast Simulation of Channel Noise in Conductance-Based Model Neurons by Diffusion Approximation," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-17, March.
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