The Brain Simulation Platform "Live Papers"
Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties

Authors: Alice Geminiani 1,2, Alessandra Pedrocchi 2, Egidio D'Angelo 1,3, Claudia Casellato 1

Author information: 1 Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, I-27100, Pavia, Italy, 2 NEARLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, via Ponzio, 34, 20133, Milan, Italy, 3 Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy,

Corresponding authors: Alice Geminiani ( )

Journal: Frontiers in Neuroinformatics

Download Url:

Citation: Geminiani A, Pedrocchi A, D’Angelo E and Casellato C (2019) Response Dynamics in an Olivocerebellar Spiking Neural Network With Non-linear Neuron Properties. Front. Comput. Neurosci. 13:68.


Licence: the Creative Commons Attribution (CC BY) license  applies for all files. Under this Open Access license anyone may copy, distribute, or reuse the files as long as the authors and the original source are properly cited.

Sensorimotor signals are integrated and processed by the cerebellar circuit to predict accurate control of actions. In order to investigate how single neuron dynamics and geometrical modular connectivity affect cerebellar processing, we have built an olivocerebellar Spiking Neural Network (SNN) based on a novel simplification algorithm for single point models (Extended Generalized Leaky Integrate and Fire, EGLIF) capturing essential non-linear neuronal dynamics (e.g., pacemaking, bursting, adaptation, oscillation and resonance). EGLIF models specifically tuned for each neuron type were embedded into an olivocerebellar scaffold reproducing realistic spatial organization and physiological convergence and divergence ratios of connections. In order to emulate the circuit involved in an eye blink response to two associated stimuli, we modeled two adjacent olivocerebellar microcomplexes with a common mossy fiber input but different climbing fiber inputs (either on or off). EGLIF-SNN model simulations revealed the emergence of fundamental response properties in Purkinje cells (burst-pause) and deep nuclei cells (pause-burst) similar to those reported in vivo. The expression of these properties depended on the specific activation of climbing fibers in the microcomplexes and did not emerge with scaffold models using simplified point neurons. This result supports the importance of embedding SNNs with realistic neuronal dynamics and appropriate connectivity and anticipates the scale-up of EGLIF-SNN and the embedding of plasticity rules required to investigate cerebellar functioning at multiple scales.

Data and models: all data and models used in the paper are available at the links reported below, grouped into the following categories: