标题： 关于尖峰时间的信息：从多时序组中派生的神经编码 显示英文标题

标题： On the information in spike timing: neural codes derived from polychronous groups

摘要： 越来越多的证据表明，尖峰时间在神经信息处理中非常重要，即使少量的尖峰也能携带信息，但计算模型在这一方面明显落后于速率编码的模型。 关于神经元行为的实验证据与由循环连接提供的动态和状态依赖行为是一致的。 这促使了本文所研究的极简抽象，旨在通过循环连接提供对尖峰时间中信息编码的见解。 我们采用信息论技术对一个简单的储备模型进行研究，该模型将输入的时空模式编码为稀疏的神经代码，将Izhikevich引入的多时程组转化为可以执行标准向量操作的码字。 我们证明了该代码的距离特性与（最优）随机代码的特性相似。 特别是，该代码满足与线性分类和容量相关的基准，其中后者随着储备大小呈指数级增长。 显示英文摘要

摘要： There is growing evidence regarding the importance of spike timing in neural information processing, with even a small number of spikes carrying information, but computational models lag significantly behind those for rate coding. Experimental evidence on neuronal behavior is consistent with the dynamical and state dependent behavior provided by recurrent connections. This motivates the minimalistic abstraction investigated in this paper, aimed at providing insight into information encoding in spike timing via recurrent connections. We employ information-theoretic techniques for a simple reservoir model which encodes input spatiotemporal patterns into a sparse neural code, translating the polychronous groups introduced by Izhikevich into codewords on which we can perform standard vector operations. We show that the distance properties of the code are similar to those for (optimal) random codes. In particular, the code meets benchmarks associated with both linear classification and capacity, with the latter scaling exponentially with reservoir size.