Chaos may enhance expressivity in cerebellar granular layer
Recent evidence suggests that Golgi cells in the cerebellar granular layer are densely connected to each other with massive gap junctions. Here, we propose that the massive gap junctions between the Golgi cells contribute to the representational complexity of the granular layer of the cerebellum by...
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| Veröffentlicht in: | Neural networks 2021-04, Vol.136, p.72-86 |
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| creator | Tokuda, Keita Fujiwara, Naoya Sudo, Akihito Katori, Yuichi |
| description | Recent evidence suggests that Golgi cells in the cerebellar granular layer are densely connected to each other with massive gap junctions. Here, we propose that the massive gap junctions between the Golgi cells contribute to the representational complexity of the granular layer of the cerebellum by inducing chaotic dynamics. We construct a model of cerebellar granular layer with diffusion coupling through gap junctions between the Golgi cells, and evaluate the representational capability of the network with the reservoir computing framework. First, we show that the chaotic dynamics induced by diffusion coupling results in complex output patterns containing a wide range of frequency components. Second, the long non-recursive time series of the reservoir represents the passage of time from an external input. These properties of the reservoir enable mapping different spatial inputs into different temporal patterns. |
| doi_str_mv | 10.1016/j.neunet.2020.12.020 |
| format | Article |
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Here, we propose that the massive gap junctions between the Golgi cells contribute to the representational complexity of the granular layer of the cerebellum by inducing chaotic dynamics. We construct a model of cerebellar granular layer with diffusion coupling through gap junctions between the Golgi cells, and evaluate the representational capability of the network with the reservoir computing framework. First, we show that the chaotic dynamics induced by diffusion coupling results in complex output patterns containing a wide range of frequency components. Second, the long non-recursive time series of the reservoir represents the passage of time from an external input. 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| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Animals Cerebellar Cortex - cytology Cerebellar Cortex - physiology Cerebellar Golgi Cells - physiology Cerebellar granular layer Cerebellum - cytology Cerebellum - physiology Chaotic dynamics Gap junction Gap Junctions - physiology Humans Nerve Net - cytology Nerve Net - physiology Nonlinear Dynamics Reaction–diffusion system Reservoir computing Sierpinski gasket |
| title | Chaos may enhance expressivity in cerebellar granular layer |
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