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Cattani A, Solinas S, Canuto C. A Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer. Front Comput Neurosci. 2016;10:30doi: 10.3389/fncom.2016.00030.
Cattani, A., Solinas, S., & Canuto, C. (2016). A Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer. Frontiers in computational neuroscience, 1030. https://doi.org/10.3389/fncom.2016.00030
Cattani, Anna, et al. "A Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer." Frontiers in computational neuroscience vol. 10 (2016): 30. doi: https://doi.org/10.3389/fncom.2016.00030
Cattani A, Solinas S, Canuto C. A Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer. Front Comput Neurosci. 2016 Apr 19;10:30. doi: 10.3389/fncom.2016.00030. eCollection 2016. PMID: 27148027; PMCID: PMC4837690.
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Front Comput Neurosci. 2016 Apr 19;10:30. doi: 10.3389/fncom.2016.00030. eCollection 2016.

A Hybrid Model for the Computationally-Efficient Simulation of the Cerebellar Granular Layer.

Frontiers in computational neuroscience

Anna Cattani, Sergio Solinas, Claudio Canuto

Affiliations

  1. Laboratory of Neural Computation, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia Rovereto, Italy.
  2. Department of Brain and Behavioural Science, University of Pavia Pavia, Italy.
  3. Department of Mathematical Sciences, Polytechnic University of Turin Torino, Italy.

PMID: 27148027 PMCID: PMC4837690 DOI: 10.3389/fncom.2016.00030

Abstract

The aim of the present paper is to efficiently describe the membrane potential dynamics of neural populations formed by species having a high density difference in specific brain areas. We propose a hybrid model whose main ingredients are a conductance-based model (ODE system) and its continuous counterpart (PDE system) obtained through a limit process in which the number of neurons confined in a bounded region of the brain tissue is sent to infinity. Specifically, in the discrete model, each cell is described by a set of time-dependent variables, whereas in the continuum model, cells are grouped into populations that are described by a set of continuous variables. Communications between populations, which translate into interactions among the discrete and the continuous models, are the essence of the hybrid model we present here. The cerebellum and cerebellum-like structures show in their granular layer a large difference in the relative density of neuronal species making them a natural testing ground for our hybrid model. By reconstructing the ensemble activity of the cerebellar granular layer network and by comparing our results to a more realistic computational network, we demonstrate that our description of the network activity, even though it is not biophysically detailed, is still capable of reproducing salient features of neural network dynamics. Our modeling approach yields a significant computational cost reduction by increasing the simulation speed at least 270 times. The hybrid model reproduces interesting dynamics such as local microcircuit synchronization, traveling waves, center-surround, and time-windowing.

Keywords: cerebellum; conductance-based models; continuum models; hybrid models; neural networks

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