Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network

Reconstructing neuronal microcircuits through computational models is fundamental to simulate local neuronal dynamics. Here a scaffold model of the cerebellum has been developed in order to flexibly place neurons in space, connect them synaptically, and endow neurons and synapses with biologically-g...

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Autores principales: Casali, Stefano, Marenzi, Elisa, Medini, Chaitanya, Casellato, Claudia, D'Angelo, Egidio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6530631/
https://www.ncbi.nlm.nih.gov/pubmed/31156416
http://dx.doi.org/10.3389/fninf.2019.00037
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author Casali, Stefano
Marenzi, Elisa
Medini, Chaitanya
Casellato, Claudia
D'Angelo, Egidio
author_facet Casali, Stefano
Marenzi, Elisa
Medini, Chaitanya
Casellato, Claudia
D'Angelo, Egidio
author_sort Casali, Stefano
collection PubMed
description Reconstructing neuronal microcircuits through computational models is fundamental to simulate local neuronal dynamics. Here a scaffold model of the cerebellum has been developed in order to flexibly place neurons in space, connect them synaptically, and endow neurons and synapses with biologically-grounded mechanisms. The scaffold model can keep neuronal morphology separated from network connectivity, which can in turn be obtained from convergence/divergence ratios and axonal/dendritic field 3D geometries. We first tested the scaffold on the cerebellar microcircuit, which presents a challenging 3D organization, at the same time providing appropriate datasets to validate emerging network behaviors. The scaffold was designed to integrate the cerebellar cortex with deep cerebellar nuclei (DCN), including different neuronal types: Golgi cells, granule cells, Purkinje cells, stellate cells, basket cells, and DCN principal cells. Mossy fiber inputs were conveyed through the glomeruli. An anisotropic volume (0.077 mm(3)) of mouse cerebellum was reconstructed, in which point-neuron models were tuned toward the specific discharge properties of neurons and were connected by exponentially decaying excitatory and inhibitory synapses. Simulations using both pyNEST and pyNEURON showed the emergence of organized spatio-temporal patterns of neuronal activity similar to those revealed experimentally in response to background noise and burst stimulation of mossy fiber bundles. Different configurations of granular and molecular layer connectivity consistently modified neuronal activation patterns, revealing the importance of structural constraints for cerebellar network functioning. The scaffold provided thus an effective workflow accounting for the complex architecture of the cerebellar network. In principle, the scaffold can incorporate cellular mechanisms at multiple levels of detail and be tuned to test different structural and functional hypotheses. A future implementation using detailed 3D multi-compartment neuron models and dynamic synapses will be needed to investigate the impact of single neuron properties on network computation.
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spelling pubmed-65306312019-05-31 Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network Casali, Stefano Marenzi, Elisa Medini, Chaitanya Casellato, Claudia D'Angelo, Egidio Front Neuroinform Neuroscience Reconstructing neuronal microcircuits through computational models is fundamental to simulate local neuronal dynamics. Here a scaffold model of the cerebellum has been developed in order to flexibly place neurons in space, connect them synaptically, and endow neurons and synapses with biologically-grounded mechanisms. The scaffold model can keep neuronal morphology separated from network connectivity, which can in turn be obtained from convergence/divergence ratios and axonal/dendritic field 3D geometries. We first tested the scaffold on the cerebellar microcircuit, which presents a challenging 3D organization, at the same time providing appropriate datasets to validate emerging network behaviors. The scaffold was designed to integrate the cerebellar cortex with deep cerebellar nuclei (DCN), including different neuronal types: Golgi cells, granule cells, Purkinje cells, stellate cells, basket cells, and DCN principal cells. Mossy fiber inputs were conveyed through the glomeruli. An anisotropic volume (0.077 mm(3)) of mouse cerebellum was reconstructed, in which point-neuron models were tuned toward the specific discharge properties of neurons and were connected by exponentially decaying excitatory and inhibitory synapses. Simulations using both pyNEST and pyNEURON showed the emergence of organized spatio-temporal patterns of neuronal activity similar to those revealed experimentally in response to background noise and burst stimulation of mossy fiber bundles. Different configurations of granular and molecular layer connectivity consistently modified neuronal activation patterns, revealing the importance of structural constraints for cerebellar network functioning. The scaffold provided thus an effective workflow accounting for the complex architecture of the cerebellar network. In principle, the scaffold can incorporate cellular mechanisms at multiple levels of detail and be tuned to test different structural and functional hypotheses. A future implementation using detailed 3D multi-compartment neuron models and dynamic synapses will be needed to investigate the impact of single neuron properties on network computation. Frontiers Media S.A. 2019-05-15 /pmc/articles/PMC6530631/ /pubmed/31156416 http://dx.doi.org/10.3389/fninf.2019.00037 Text en Copyright © 2019 Casali, Marenzi, Medini, Casellato and D'Angelo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Casali, Stefano
Marenzi, Elisa
Medini, Chaitanya
Casellato, Claudia
D'Angelo, Egidio
Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network
title Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network
title_full Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network
title_fullStr Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network
title_full_unstemmed Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network
title_short Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network
title_sort reconstruction and simulation of a scaffold model of the cerebellar network
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6530631/
https://www.ncbi.nlm.nih.gov/pubmed/31156416
http://dx.doi.org/10.3389/fninf.2019.00037
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