The Cerebellum

, Volume 16, Issue 1, pp 15–25 | Cite as

Computational Architecture of the Granular Layer of Cerebellum-Like Structures

Original Paper


In the adaptive filter model of the cerebellum, the granular layer performs a recoding which expands incoming mossy fibre signals into a temporally diverse set of basis signals. The underlying neural mechanism is not well understood, although various mechanisms have been proposed, including delay lines, spectral timing and echo state networks. Here, we develop a computational simulation based on a network of leaky integrator neurons, and an adaptive filter performance measure, which allows candidate mechanisms to be compared. We demonstrate that increasing the circuit complexity improves adaptive filter performance, and relate this to evolutionary innovations in the cerebellum and cerebellum-like structures in sharks and electric fish. We show how recurrence enables an increase in basis signal duration, which suggest a possible explanation for the explosion in granule cell numbers in the mammalian cerebellum.


Cerebellum Adaptive filter Granular layer Neural network Computational simulation Cerebellum-like 


Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of AucklandAucklandNew Zealand
  2. 2.School of Biological SciencesUniversity of AucklandAucklandNew Zealand

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