论文标题
混乱可能会提高小脑颗粒层的表现力
Chaos may enhance expressivity in cerebellar granular layer
论文作者
论文摘要
最近的证据表明，小脑颗粒层中的高尔基细胞与巨大的间隙连接密密相互连接。在这里，我们建议高尔基细胞之间的巨大间隙连接通过诱导混沌动力学来促进小脑颗粒层的代表性复杂性。我们构建了一个小脑颗粒层的模型，该模型通过高尔基细胞之间的间隙连接构建扩散耦合，并通过储层计算框架评估网络的表示能力。首先，我们表明，扩散耦合引起的混乱动力学导致复杂的输出模式，其中包含广泛的频率组件。其次，储层的长期非恢复时间序列表示时间从外部输入中传递。储层的这些属性可以将不同的空间输入映射为不同的时间模式。
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.
