Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via adaptive generalized leaky integrate-and-fire models (Q6566674)
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scientific article; zbMATH DE number 7875669
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| English | Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via adaptive generalized leaky integrate-and-fire models |
scientific article; zbMATH DE number 7875669 |
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Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via adaptive generalized leaky integrate-and-fire models (English)
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3 July 2024
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In a previous paper, [\textit{A. Marasco} et al., Bull. Math. Biol. 85, No. 11, Paper No. 109, 38 p. (2023; Zbl 1530.92012)], the A-GLIF model to describe the dynamics, in a subthreshold regime of the membrane potential V in response to constant and piecewise constant stimulation currents has been introduced.\N\NIn the present article, one extends the A-GLIF model with some new update rules that enable the model to reproduce constant as well as variable current inputs. The referred A-GLIF model for constant and piecewise constant current injections is extensively described in the Subsection 2.2. and Appendix. Supporting Information of the present paper.\N\NThe new A-GLIF model for time-varying stimulation currents is then introduced in the Subsection 2.3. in form of a nonautonomous system of differential equations,\N\[\N\frac{dV}{dt}=\alpha(t)+\beta(I_{\mathrm{dep}}-I_{\mathrm{adap}})+\delta(1+V) +F(t),\N\]\N\begin{align*}\N\frac{I_{\mathrm{adap}}}{dt}&= 1-I_{\mathrm{adap}}+V+G(t),\\\N\frac{I_{\mathrm{dep}}}{dt}&=-\beta I_{\mathrm{dep}}\N\end{align*}\Ntogether with additional initial conditions and some update rules. Statistical analysis, performance measures, implementation techniques, simulation results and model validation are largely presented. A general discussion on obtained results is performed at the end of the article.
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neuronal modeling
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generalized leaky integrate-and-fire models
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large-scale neuronal network
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hippocampal CA1 pyramidal neurons and interneurons
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