Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via adaptive generalized leaky integrate-and-fire models. (English) Zbl 1542.92005
In a previous paper, [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.
In 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.
The 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, \[ \frac{dV}{dt}=\alpha(t)+\beta(I_{\mathrm{dep}}-I_{\mathrm{adap}})+\delta(1+V) +F(t), \] \begin{align*} \frac{I_{\mathrm{adap}}}{dt}&= 1-I_{\mathrm{adap}}+V+G(t),\\ \frac{I_{\mathrm{dep}}}{dt}&=-\beta I_{\mathrm{dep}} \end{align*} together 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.
In 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.
The 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, \[ \frac{dV}{dt}=\alpha(t)+\beta(I_{\mathrm{dep}}-I_{\mathrm{adap}})+\delta(1+V) +F(t), \] \begin{align*} \frac{I_{\mathrm{adap}}}{dt}&= 1-I_{\mathrm{adap}}+V+G(t),\\ \frac{I_{\mathrm{dep}}}{dt}&=-\beta I_{\mathrm{dep}} \end{align*} together 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.
Reviewer: Claudia Simionescu-Badea (Wien)
MSC:
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
Keywords:
neuronal modeling; generalized leaky integrate-and-fire models; large-scale neuronal network; hippocampal CA1 pyramidal neurons and interneuronsCitations:
Zbl 1530.92012Software:
NEURONReferences:
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