Polychronization: Computation with spikes
Polychronization: Computation with spikes
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DOI：
10.1162/089976606775093882
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发表时间：
2006-02-01
影响因子：
2.9
通讯作者：
Izhikevich, Eugene M.
中科院分区：
文献类型：
--
作者：
Izhikevich, Eugene M.
我们提出了一种可以多同步的最小尖峰网络，即表现出可重复的时间锁定但不具有毫秒精度的同步发射模式，如 Synfire 辫子。该网络由具有轴突传导延迟和尖峰时序依赖性可塑性（STDP）的皮质尖峰神经元组成；包含即用型 MATLAB 代码。它表现出睡眠般的振荡、伽玛（40 Hz）节律、放电频率到尖峰时间的转换以及其他有趣的机制。由于延迟和 STDP 之间的相互作用，尖峰神经元自发地自我组织成组并产生典型的多时活动模式。令我们惊讶的是，共存的多时组的数量远远超过了网络中神经元的数量，导致系统的记忆容量达到了前所未有的水平。我们推测多时性对神经元群体选择理论（TNGS，神经达尔文主义）、认知神经计算、结合和伽马节律、注意力机制以及“对记忆的注意力”的意识的重要性。
We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as "attention to memories.".