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

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.

Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties

“…The basal activity of cerebellar neurons and their response to MF and CF inputs is illustrated in Figures 2–7. In both EGLIF-SNN and LIF-SNN models, during baseline MF activation with random noise at 4 Hz (Rancz et al, 2007), the GrCs were driven into low frequency firing, and the GoC, MLI, PC and DCN neurons slightly increased their firing rate compared to intrinsic pacemaking (Geminiani et al, 2019).…”

“…Single neurons in the SNN were modeled as EGLIF, able to reproduce the full set of spiking patterns of cerebellar neurons (Geminiani et al, 2018b). In details, a cell-specific parameter set was applied to meet the electroresponsive phenotype of each olivocerebellar neuron (e.g., GoC: autorhythm, adaptation, rebound bursting, phase reset, subthreshold oscillations, resonance; GrC: subthreshold oscillations and resonance; PC: autorhythm and bursting; DCN: autorhythm, adaptation and rebound bursting; IO: subthreshold oscillations, rebound spiking, phase reset), as optimized in Geminiani et al (2019) (Supplementary Material). Only the firing irregularity parameters were modified with respect to Geminiani et al (2019), to account during network simulations for higher noise components that are absent during in vitro experiments (Supplementary Material).…”

“…In details, a cell-specific parameter set was applied to meet the electroresponsive phenotype of each olivocerebellar neuron (e.g., GoC: autorhythm, adaptation, rebound bursting, phase reset, subthreshold oscillations, resonance; GrC: subthreshold oscillations and resonance; PC: autorhythm and bursting; DCN: autorhythm, adaptation and rebound bursting; IO: subthreshold oscillations, rebound spiking, phase reset), as optimized in Geminiani et al (2019) (Supplementary Material). Only the firing irregularity parameters were modified with respect to Geminiani et al (2019), to account during network simulations for higher noise components that are absent during in vitro experiments (Supplementary Material). As a result, we obtained physiological Coefficient of Variation of Inter-Spike Intervals (CV ISI ) and average firing frequency ( f tonic ) observed in vivo (Ten Brinke et al, 2017; Boele et al, 2018).…”

“…The reconstructed olivocerebellar network with optimized cell-specific neuron models (Geminiani et al, 2019) was then simulated in PyNEST (Diesmann and Gewaltig, 2002; Eppler et al, 2009). The emergent spatio-temporal dynamics was analyzed, such as the responses of all neuron populations to sensory signals involving different input pathways.…”

“…The first one is the Extended-Generalized Leaky Integrate and Fire (EGLIF) point neuron that maintains salient electrophysiological features – autorhythm, bursts, adaptation, oscillations and resonance – by using just a few state variables (Geminiani et al, 2018b). The EGLIF proved capable to reproduce the rich set of firing patterns of the main olivocerebellar neurons: GoCs, GrCs, PCs, MLIs, DCNs, and IO (Geminiani et al, 2019). The second aspect is network geometry derived from a cerebellar scaffold model, which reproduces the physiological convergence and divergence ratios of connections with a realistic spatial organization (Casali et al, 2019).…”