Estimation of Synaptic Activity during Neuronal Oscillations

Catalina Vich, Rafel Prohens, Antonio E. Teruel and Antoni Guillamon
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Catalina Vich: Departament de Matemàtiques i Informàtica, Institute of Applied Computing and Community Code, Universitat de les Illes Balears, Palma, E-07122 Illes Balears, Spain
Rafel Prohens: Departament de Matemàtiques i Informàtica, Institute of Applied Computing and Community Code, Universitat de les Illes Balears, Palma, E-07122 Illes Balears, Spain
Antonio E. Teruel: Departament de Matemàtiques i Informàtica, Institute of Applied Computing and Community Code, Universitat de les Illes Balears, Palma, E-07122 Illes Balears, Spain
Antoni Guillamon: Department de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, E-08028 Catalunya, Spain

Mathematics, 2020, vol. 8, issue 12, 1-22

Abstract: In the study of brain connectivity, an accessible and convenient way to unveil local functional structures is to infer the time trace of synaptic conductances received by a neuron by using exclusively information about its membrane potential (or voltage). Mathematically speaking, it constitutes a challenging inverse problem: it consists in inferring time-dependent parameters (synaptic conductances) departing from the solutions of a dynamical system that models the neuron’s membrane voltage. Several solutions have been proposed to perform these estimations when the neuron fluctuates mildly within the subthreshold regime, but very few methods exist for the spiking regime as large amplitude oscillations (revealing the activation of complex nonlinear dynamics) hinder the adaptability of subthreshold-based computational strategies (mostly linear). In a previous work, we presented a mathematical proof-of-concept that exploits the analytical knowledge of the period function of the model. Inspired by the relevance of the period function, in this paper we generalize it by providing a computational strategy that can potentially adapt to a variety of models as well as to experimental data. We base our proposal on the frequency versus synaptic conductance curve ( f − g s y n ), derived from an analytical study of a base model, to infer the actual synaptic conductance from the interspike intervals of the recorded voltage trace. Our results show that, when the conductances do not change abruptly on a time-scale smaller than the mean interspike interval, the time course of the synaptic conductances is well estimated. When no base model can be cast to the data, our strategy can be applied provided that a suitable f − g s y n table can be experimentally constructed. Altogether, this work opens new avenues to unveil local brain connectivity in spiking (nonlinear) regimes.

Keywords: inverse problem; brain connectivity; modeling of neuronal activity; synaptic conductance estimation; spiking activity; intracellular recordings (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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