Chaos may enhance expressivity in cerebellar granular layer
This addresses a problem in neuroscience for understanding cerebellar computation, but it appears incremental as it builds on existing models with specific modifications.
The study tackled how chaotic dynamics in the cerebellar granular layer, induced by gap junctions between Golgi cells, enhance representational complexity, showing that this leads to complex output patterns with wide frequency ranges and enables mapping spatial inputs into distinct temporal patterns.
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.