利用突触可塑性的自组织
Self-organization using synaptic plasticity
摘要：
尖峰神经元的大型网络显示出集体动力学的突然变化，类似于统计物理学中研究的相变。这种现象的一个例子是，随着神经元之间的相互作用强度的增加，在整合和激发神经元网络中观察到的从不规则的噪声驱动动力学到规则的自我维持行为的转变。在这项工作中，我们展示了一个网络的尖峰神经元是如何能够自我组织的临界状态，其中可能的区间尖峰间隔（动态范围）的范围是最大化的。自组织通过我们分析得出的突触动力学发生。由此产生的可塑性规则是局部定义的，以便通过对单个突触的局部调节来实现临界状态附近的全局稳态。
英文摘要：
Large networks of spiking neurons show abrupt changes in their collective dynamics resembling phase transitions studied in statistical physics. An example of this phenomenon is the transition from irregular, noise-driven dynamics to regular, self-sustained behavior observed in networks of integrate-and-fire neurons as the interaction strength between the neurons increases. In this work we show how a network of spiking neurons is able to self-organize towards a critical state for which the range of possible inter-spike-intervals (dynamic range) is maximized. Self-organization occurs via synaptic dynamics that we analytically derive. The resulting plasticity rule is defined locally so that global homeostasis near the critical state is achieved by local regulation of individual synapses.
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