Modeling the cerebellar microcircuit

New strategies for a long-standing issue

Egidio D’Angelo, Alberto Antonietti, Stefano Casali, Claudia Casellato, Jesus A. Garrido, Niceto Rafael Luque, Lisa Mapelli, Stefano Masoli, Alessandra Pedrocchi, Francesca Prestori, Martina Francesca Rizza, Eduardo Ros

Research output: Contribution to journalReview article

20 Citations (Scopus)

Abstract

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

Original languageEnglish
Article number176
JournalFrontiers in Cellular Neuroscience
Volume10
Issue numberJULY
DOIs
Publication statusPublished - Jul 8 2016

Fingerprint

Neuronal Plasticity
Nonlinear Dynamics
Cerebellum
Learning
Membranes
Research

Keywords

  • Cellular neurophysiology
  • Cerebellum
  • Computational modeling
  • Microcircuit
  • Motor learning
  • Neural plasticity
  • Neurorobotics
  • Spiking neural network

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience

Cite this

D’Angelo, E., Antonietti, A., Casali, S., Casellato, C., Garrido, J. A., Luque, N. R., ... Ros, E. (2016). Modeling the cerebellar microcircuit: New strategies for a long-standing issue. Frontiers in Cellular Neuroscience, 10(JULY), [176]. https://doi.org/10.3389/fncel.2016.00176

Modeling the cerebellar microcircuit : New strategies for a long-standing issue. / D’Angelo, Egidio; Antonietti, Alberto; Casali, Stefano; Casellato, Claudia; Garrido, Jesus A.; Luque, Niceto Rafael; Mapelli, Lisa; Masoli, Stefano; Pedrocchi, Alessandra; Prestori, Francesca; Rizza, Martina Francesca; Ros, Eduardo.

In: Frontiers in Cellular Neuroscience, Vol. 10, No. JULY, 176, 08.07.2016.

Research output: Contribution to journalReview article

D’Angelo, E, Antonietti, A, Casali, S, Casellato, C, Garrido, JA, Luque, NR, Mapelli, L, Masoli, S, Pedrocchi, A, Prestori, F, Rizza, MF & Ros, E 2016, 'Modeling the cerebellar microcircuit: New strategies for a long-standing issue', Frontiers in Cellular Neuroscience, vol. 10, no. JULY, 176. https://doi.org/10.3389/fncel.2016.00176
D’Angelo, Egidio ; Antonietti, Alberto ; Casali, Stefano ; Casellato, Claudia ; Garrido, Jesus A. ; Luque, Niceto Rafael ; Mapelli, Lisa ; Masoli, Stefano ; Pedrocchi, Alessandra ; Prestori, Francesca ; Rizza, Martina Francesca ; Ros, Eduardo. / Modeling the cerebellar microcircuit : New strategies for a long-standing issue. In: Frontiers in Cellular Neuroscience. 2016 ; Vol. 10, No. JULY.
@article{a6b5059a7d1f425cb96b11c1e1628b9e,
title = "Modeling the cerebellar microcircuit: New strategies for a long-standing issue",
abstract = "The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.",
keywords = "Cellular neurophysiology, Cerebellum, Computational modeling, Microcircuit, Motor learning, Neural plasticity, Neurorobotics, Spiking neural network",
author = "Egidio D’Angelo and Alberto Antonietti and Stefano Casali and Claudia Casellato and Garrido, {Jesus A.} and Luque, {Niceto Rafael} and Lisa Mapelli and Stefano Masoli and Alessandra Pedrocchi and Francesca Prestori and Rizza, {Martina Francesca} and Eduardo Ros",
year = "2016",
month = "7",
day = "8",
doi = "10.3389/fncel.2016.00176",
language = "English",
volume = "10",
journal = "Frontiers in Cellular Neuroscience",
issn = "1662-5102",
publisher = "Frontiers Media S.A.",
number = "JULY",

}

TY - JOUR

T1 - Modeling the cerebellar microcircuit

T2 - New strategies for a long-standing issue

AU - D’Angelo, Egidio

AU - Antonietti, Alberto

AU - Casali, Stefano

AU - Casellato, Claudia

AU - Garrido, Jesus A.

AU - Luque, Niceto Rafael

AU - Mapelli, Lisa

AU - Masoli, Stefano

AU - Pedrocchi, Alessandra

AU - Prestori, Francesca

AU - Rizza, Martina Francesca

AU - Ros, Eduardo

PY - 2016/7/8

Y1 - 2016/7/8

N2 - The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

AB - The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

KW - Cellular neurophysiology

KW - Cerebellum

KW - Computational modeling

KW - Microcircuit

KW - Motor learning

KW - Neural plasticity

KW - Neurorobotics

KW - Spiking neural network

UR - http://www.scopus.com/inward/record.url?scp=84978765955&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84978765955&partnerID=8YFLogxK

U2 - 10.3389/fncel.2016.00176

DO - 10.3389/fncel.2016.00176

M3 - Review article

VL - 10

JO - Frontiers in Cellular Neuroscience

JF - Frontiers in Cellular Neuroscience

SN - 1662-5102

IS - JULY

M1 - 176

ER -