Neural Modeling of an internal clock
Neural Modeling of an internal clock
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DOI：
10.1162/0899766053491850
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发表时间：
2005-05-01
影响因子：
2.9
通讯作者：
Tanaka, S
中科院分区：
文献类型：
--
作者：
Yamazaki, T;Tanaka, S
研究了一个简单的随机递归抑制网络。尽管它很简单，但动力学是如此丰富，以至于神经元的活动模式随着时间的推移而演变，由于神经元之间的随机循环连接而不会复发。活动模式的序列是由外部信号的触发产生的，并且该产生对于噪声是稳定的。此外，使用强瞬态信号可再现相同的序列，即，序列生成可被重置。因此，从外部信号触发开始的时间流逝可以由活动模式的序列表示，这表明该模型可以作为内部时钟工作。该模型可以通过提供不同的外部信号产生不同的活动模式序列，从而可以表示时空信息。此外，可以加快和减慢序列生成。
We studied a simple random recurrent inhibitory network. Despite its simplicity, the dynamics was so rich that activity patterns of neurons evolved with time without recurrence due to random recurrent connections among neurons. The sequence of activity patterns was generated by the trigger of an external signal, and the generation was stable against noise. Moreover, the same sequence was reproducible using a strong transient signal, that is, the sequence generation could be reset. Therefore, a time passage from the trigger of an external signal could be represented by the sequence of activity patterns, suggesting that this model could work as an internal clock. The model could generate different sequences of activity patterns by providing different external signals; thus, spatiotemporal information could be represented by this model. Moreover, it was possible to speed up and slow down the sequence generation.