Computational Architecture of the Granular Layer of Cerebellum-Like Structures
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- 作者：Peter Bratby ; James Sneyd ; John Montgomery
- 关键词：Cerebellum ; Adaptive filter ; Granular layer ; Neural network ; Computational simulation ; Cerebellum ; like
- 刊名：The Cerebellum
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：16
- 期：1
- 页码：15-25
- 全文大小：
- 刊物主题：Neurosciences; Neurology; Neurobiology;
- 出版者：Springer US
- ISSN：1473-4230
- 卷排序：16
文摘
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.