  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.
