AUTHOR=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 TITLE=Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue JOURNAL=Frontiers in Cellular Neuroscience VOLUME=10 YEAR=2016 URL=https://www.frontiersin.org/journals/cellular-neuroscience/articles/10.3389/fncel.2016.00176 DOI=10.3389/fncel.2016.00176 ISSN=1662-5102 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.