Ernst, Ariane and Schütte, Christof and Sigrist, Stephan J. and Winkelmann, Stefanie
(2022)
*Variance of filtered signals: Characterization for linear reaction networks and application to neurotransmission dynamics.*
Mathematical Biosciences, 343
.

Full text not available from this repository.

Official URL: https://doi.org/10.1016/j.mbs.2021.108760

## Abstract

Neurotransmission at chemical synapses relies on the calcium-induced fusion of synaptic vesicles with the presynaptic membrane. The distance of the synaptic vesicle to the calcium channels determines the release probability and consequently the postsynaptic signal. Suitable models of the process need to capture both the mean and the variance observed in electrophysiological measurements of the postsynaptic current. In this work, we propose a method to directly compute the exact first- and second-order moments for signals generated by a linear reaction network under convolution with an impulse response function, rendering computationally expensive numerical simulations of the underlying stochastic counting process obsolete. We show that the autocorrelation of the process is central for the calculation of the filtered signal’s second-order moments, and derive a system of PDEs for the cross-correlation functions (including the autocorrelations) of linear reaction networks with time-dependent rates. Finally, we employ our method to efficiently compare different spatial coarse graining approaches for a specific model of synaptic vesicle fusion. Beyond the application to neurotransmission processes, the developed theory can be applied to any linear reaction system that produces a filtered stochastic signal.

Item Type: | Article |
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Subjects: | Mathematical and Computer Sciences Mathematical and Computer Sciences > Mathematics Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics |

ID Code: | 2961 |

Deposited By: | Monika Drueck |

Deposited On: | 20 Apr 2023 10:41 |

Last Modified: | 20 Apr 2023 10:41 |

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