At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters

PLoS Comput Biol. 2015 Oct 20;11(10):e1004515. doi: 10.1371/journal.pcbi.1004515. eCollection 2015 Oct.


Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations. Details of this process are however unclear. While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets, the more general application for temporally varying signals has yet to be demonstrated. Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants. The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior. We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells. Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells, variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway. Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology*
  • Animals
  • Cerebellum / physiology*
  • Computer Simulation
  • Humans
  • Models, Neurological*
  • Models, Statistical
  • Nerve Net / physiology*
  • Neural Inhibition / physiology*
  • Neurons / physiology*
  • Nonlinear Dynamics

Grant support

This work was supported by a grant from the European Union 2012 (REALNET, 270434 FP7) and partially supported by EPSRC grant no. EP/IO32533/1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.