Complex Electroresponsive Dynamics in Olivocerebellar Neurons Represented With Extended-Generalized Leaky Integrate and Fire Models.

Alice Geminiani,Egidio D’Angelo,Claudia Casellato,Alessandra Pedrocchi

doi:10.3389/fncom.2019.00035

Alice Geminiani, Egidio D’Angelo + Show 2 more

Open Access

https://doi.org/10.3389/fncom.2019.00035

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Abstract

The neurons of the olivocerebellar circuit exhibit complex electroresponsive dynamics, which are thought to play a fundamental role for network entraining, plasticity induction, signal processing, and noise filtering. In order to reproduce these properties in single-point neuron models, we have optimized the Extended-Generalized Leaky Integrate and Fire (E-GLIF) neuron through a multi-objective gradient-based algorithm targeting the desired input–output relationships. In this way, E-GLIF was tuned toward the unique input–output properties of Golgi cells, granule cells, Purkinje cells, molecular layer interneurons, deep cerebellar nuclei cells, and inferior olivary cells. E-GLIF proved able to simulate the complex cell-specific electroresponsive dynamics of the main olivocerebellar neurons including pacemaking, adaptation, bursting, post-inhibitory rebound excitation, subthreshold oscillations, resonance, and phase reset. The integration of these E-GLIF point-neuron models into olivocerebellar Spiking Neural Networks will allow to evaluate the impact of complex electroresponsive dynamics at the higher scales, up to motor behavior, in closed-loop simulations of sensorimotor tasks.

Highlights

The variety of neuron types and spiking patterns is thought to play a fundamental role for cerebellar signal processing (Llinás, 1988, 2014) and eventually for motor learning and control
The results shown here are fundamental in view of Spiking Neural Networks (SNNs) simulations where the impact of complex single neuron dynamics will be evaluated at the network and, eventually, at the behavioral level (D’Angelo et al, 2016a)
To reproduce the firing patterns described in the Section “Introduction,” single neurons were modeled as Extended-Generalized Leaky Integrate and Fire (E-GLIF) point neurons

Summary

Introduction

The variety of neuron types and spiking patterns is thought to play a fundamental role for cerebellar signal processing (Llinás, 1988, 2014) and eventually for motor learning and control. The Golgi Cells (GoCs) show spike-frequency adaptation (SFA) when depolarized by prolonged currents, post-inhibitory rebound bursts, phase reset, sub-threshold oscillations (STO), and resonance in theta band (Solinas et al, 2007a,b). Molecular Layer Interneurons (MLIs) fire spontaneously with an increased firing irregularity in vivo (Lachamp et al, 2009; Jörntell et al, 2010) and have no significant SFA (Galliano et al, 2013). These properties derive from the specific set of ionic channels and from their localization on neuronal dendrites, soma and axons, as well as from the specific nature of synaptic inputs

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Conclusion