Computational Neuroscience of Timing, Plasticity and Function in Cerebellum Microcircuits

  • Shyam DiwakarEmail author
  • Chaitanya Medini
  • Manjusha Nair
  • Harilal Parasuram
  • Asha Vijayan
  • Bipin Nair
Part of the Springer Series in Bio-/Neuroinformatics book series (SSBN, volume 6)


Cerebellum has been known to show homogeneity in circuit organization and hence the “modules” or various circuits in the cerebellum are attributed to the diversity of functions such as timing, pattern recognition, movement planning and dysfunctions such as ataxia related to the cerebellum. Ataxia-like conditions, induced by intrinsic excitability changes, disable spiking or bursts and thereby limit the quanta of downstream information. Understanding timing, plasticity and functional roles of cerebellum involve large-scale and microcircuit reconstructions validating molecular mechanisms in population activity. Using mathematical modelling, we attempted to reconstruct information transmission at the granular layer of the cerebellum, a circuit whose role in dysfunctions remain yet to be fully explored. We have employed spiking models to reconstruct timing roles and detailed biophysical models for extracellular activity and local field population response. The roles of inhibition, induced plasticity and their implications in information transmission were evaluated. Modulatory roles of Golgi inhibition and pattern abstraction via optimal storage were estimated. An abstraction of the granular and Purkinje layer circuit for neurorobotic roles such as pattern recognition and spike encoding via two new methods was developed. Simulations suggest plasticity at cerebellar relays may be an important element of tremendous storage capacity reliable in the learning of coordination of actions, sensorimotor or cognitive, in which the cerebellum participates.


Granule Cell Spike Train Granular Layer Mossy Fiber Granule Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work derives direction and ideas from the Chancellor of Amrita University, Sri Mata Amritanandamayi Devi. Authors would like to acknowledge Egidio D’Angelo of University of Pavia, Giovanni Naldi of University of Milan, Sergio Solinas, Thierry Nieus of IIT Genova for their support towards work in this manuscript. This work is supported by Grants SR/CSI/49/2010, SR/CSI/60/2011, SR/CSRI/60/2013, SR/CSRI/61/2014 and Indo-Italy POC 2012-2014 from DST and BT/PR5142/MED/30/764/2012 from DBT, Government of India and partially by Embracing the World.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shyam Diwakar
    • 1
    Email author
  • Chaitanya Medini
    • 1
  • Manjusha Nair
    • 1
    • 2
  • Harilal Parasuram
    • 1
  • Asha Vijayan
    • 1
  • Bipin Nair
    • 1
  1. 1.Amrita School of BiotechnologyAmrita Vishwa Vidyapeetham (Amrita University)KollamIndia
  2. 2.Amrita School of EngineeringAmrita Vishwa Vidyapeetham (Amrita University)KollamIndia

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