Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness

Frontiers in Neuroinformatics

Francesca Locatelli¹  Claudia Casellato¹  Francesca Prestori¹  Egidio D'Angelo²  Alice Geminiani³  Alessandra Pedrocchi³ 

[1] Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy;IRCCS Mondino Foundation, Pavia, Italy;NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy;

关键词: neuronal modeling;    point neuron;    leaky integrate-and-fire;    model simplification;    neuronal electroresponsiveness;    Golgi cell;   

DOI  :  10.3389/fninf.2018.00088

来源: DOAJ

【 摘 要 】

Brain neurons exhibit complex electroresponsive properties – including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset – which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in “realistic” models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. E-GLIF derives from the GLIF model family and is therefore mono-compartmental, keeps the limited computational load typical of a linear low-dimensional system, admits analytical solutions and can be tuned through gradient-descent algorithms. Importantly, E-GLIF is designed to maintain a correspondence between model parameters and neuronal membrane mechanisms through a minimum set of equations. In order to test its potential, E-GLIF was used to model a specific neuron showing rich and complex electroresponsiveness, the cerebellar Golgi cell, and was validated against experimental electrophysiological data recorded from Golgi cells in acute cerebellar slices. During simulations, E-GLIF was activated by stimulus patterns, including current steps and synaptic inputs, identical to those used for the experiments. The results demonstrate that E-GLIF can reproduce the whole set of complex neuronal dynamics typical of these neurons – including intensity-frequency curves, spike-frequency adaptation, post-inhibitory rebound bursting, spontaneous subthreshold oscillations, resonance, and phase-reset – providing a new effective tool to investigate brain dynamics in large-scale simulations.

【 授权许可】

Unknown