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 language | English |
|---|---|
| Article number | 176 |
| Journal | Frontiers in Cellular Neuroscience |
| Volume | 10 |
| Issue number | JULY |
| DOIs | |
| Publication status | Published - Jul 8 2016 |
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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
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 journal › Review article
}
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 -