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Homogenous Chaotic Network Serving as a Rate/Population Code to Temporal Code Converter

机译:均质混沌网络用作速率/人口代码到时间代码转换器

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摘要

At present, it is obvious that different sections of nervous system utilize different methods for information coding. Primary afferent signals in most cases are represented in form of spike trains using a combination of rate coding and population coding while there are clear evidences that temporal coding is used in various regions of cortex. In the present paper, it is shown that conversion between these two coding schemes can be performed under certain conditions by a homogenous chaotic neural network. Interestingly, this effect can be achieved without network training and synaptic plasticity.
机译:目前,很明显神经系统的不同部分采用了不同的信息编码方法。在大多数情况下,原始传入信号是使用速率编码和总体编码的组合以尖峰序列的形式表示的,同时有明确的证据表明在皮质的各个区域都使用了时间编码。在本文中,表明可以通过均匀的混沌神经网络在特定条件下执行这两种编码方案之间的转换。有趣的是,无需网络训练和突触可塑性即可获得这种效果。

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