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Neural excitability, spiking and bursting

机译:神经兴奋性,尖峰和爆发

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Bifurcation mechanisms involved in the generation of action potentials (spikes) by neurons are reviewed here. We show how the type of bifurcation determines the neuro-computational properties of the cells. For example, when the rest state is near a saddle-node bifurcation, the cell can fire all-or-none spikes with an arbitrary low frequency, it has a well-defined threshold manifold, and it acts as an integrator; i.e. the higher the frequency of incoming pulses, the sooner it fires. In contrast, when the rest state is near an Andronov-Hopf bifurcation, the cell fires in a certain frequency range, its spikes are not all-or-none, it dose not have a well-defined threshold manifold, it can fire in response to an inhibitory pulse, and it acts as a resonator; i.e. it responds preferentially to a certain (resonant) frequency of the input. Increasing the input frequency may actually delay or terminate its firing. We also describe the phenomenon of neural bursting, and we use geometric bifurcation theory to extend the existing classification of bursters, including many new types. We discuss how the type of burster defines its neuro-computational properties, and we show that different bursters can interact, synchronize and process information differently.
机译:在这里回顾了神经元产生动作电位(尖峰)的分叉机制。我们展示了分叉的类型如何确定细胞的神经计算特性。例如,当静止状态接近鞍形节点分叉时,该单元可以以任意低频发射全或无尖峰,它具有明确定义的阈值流形,并充当积分器;即,输入脉冲的频率越高,它发射的越早。相反,当静止状态接近Andronov-Hopf分叉时,该单元会在某个频率范围内触发,其尖峰并非全有或全无,它没有明确定义的阈值歧管,因此可以响应触发抑制脉冲,并充当谐振器;也就是说,它优先响应输入的某个(谐振)频率。增加输入频率实际上可能会延迟或终止其触发。我们还描述了神经爆裂现象,并使用几何分叉理论扩展了爆裂器的现有分类,包括许多新类型。我们讨论了爆发器的类型如何定义其神经计算特性,并且我们展示了不同的爆发器可以以不同方式交互,同步和处理信息。

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