混沌可能增强小脑颗粒层的表达能力
Chaos may enhance expressivity in cerebellar granular layer
摘要：
最近的证据表明，在小脑颗粒层的高尔基体细胞是紧密连接到彼此的巨大缝隙连接。在这里，我们提出，大量的高尔基体细胞之间的缝隙连接有助于代表复杂的颗粒层的小脑诱导混沌动力学。我们构建了一个小脑颗粒层模型，通过高尔基体细胞之间的缝隙连接扩散耦合，并评估水库计算框架的网络的代表性能力。首先，我们表明，由扩散耦合引起的混沌动力学的结果在复杂的输出模式包含一个广泛的频率分量。第二，储存器的长的非递归时间序列表示来自外部输入的时间的流逝。储层的这些特性使得能够将不同的空间输入映射到不同的时间模式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
