title: Computational Architecture of the Granular Layer of Cerebellum-Like Structures.
abstract: 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.
authors.affiliation: Department of Mathematics, University of Auckland, Auckland, New Zealand. p.bratby@auckland.ac.nz.
authors.forename: Peter
authors.initials: P
authors.lastname: Bratby
authors.affiliation: Department of Mathematics, University of Auckland, Auckland, New Zealand.
authors.forename: James
authors.initials: J
authors.lastname: Sneyd
authors.affiliation: School of Biological Sciences, University of Auckland, Auckland, New Zealand.
authors.forename: John
authors.initials: J
authors.lastname: Montgomery
mesh_terms: D000818:Animals; D005075:Biological Evolution; D002531:Cerebellum / Q000166:cytology / Q000502:physiology; D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000166:cytology / Q000502:physiology
keywords: Adaptive filter; Cerebellum; Cerebellum-like; Computational simulation; Granular layer; Neural network