由自适应脉冲神经元网络生成的超网络中的瞬态序列。
Transient sequences in a hypernetwork generated by an adaptive network of spiking neurons.
作者信息
Maslennikov Oleg V, Shchapin Dmitry S, Nekorkin Vladimir I
机构信息
Institute of Applied Physics of the Russian Academy of Sciences, 46 Ulyanov Street, 603950 Nizhny Novgorod, Russia
Institute of Applied Physics of the Russian Academy of Sciences, 46 Ulyanov Street, 603950 Nizhny Novgorod, Russia.
出版信息
Philos Trans A Math Phys Eng Sci. 2017 Jun 28;375(2096). doi: 10.1098/rsta.2016.0288.
We propose a model of an adaptive network of spiking neurons that gives rise to a hypernetwork of its dynamic states at the upper level of description. Left to itself, the network exhibits a sequence of transient clustering which relates to a traffic in the hypernetwork in the form of a random walk. Receiving inputs the system is able to generate reproducible sequences corresponding to stimulus-specific paths in the hypernetwork. We illustrate these basic notions by a simple network of discrete-time spiking neurons together with its FPGA realization and analyse their properties.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.
我们提出了一种脉冲神经元自适应网络模型，该模型在更高层次的描述中产生其动态状态的超网络。在无外部干扰的情况下，该网络呈现出一系列瞬态聚类，这与超网络中以随机游走形式存在的信息流相关。接收输入时，系统能够生成与超网络中特定刺激路径相对应的可重复序列。我们通过一个简单的离散时间脉冲神经元网络及其现场可编程门阵列（FPGA）实现来说明这些基本概念，并分析它们的特性。本文是主题为“医学中的数学方法：神经科学、心脏病学和病理学”特刊的一部分。
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