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arXiv 2011.00888nlin.AOcs.NE
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脉冲耦合振荡器网络中的受控扰动诱导开关

Controlled Perturbation-Induced Switching in Pulse-Coupled Oscillator Networks

Fabio Schittler Neves, Marc Timme

摘要:

脉冲耦合系统,如尖峰神经网络表现出非平凡不变集的形式吸引但不稳定的鞍周期轨道,其中单位同步成组。这些轨道之间的异宿连接原则上可以支持这些网络中的切换过程,并实现新型神经计算。对于耦合振荡器的小网络,我们在这里调查在哪些条件下,以及如何系统对称性强制或禁止某些开关过渡,可能会引起的扰动。对于五个振子的网络,我们推导出明确的跃迁规则,对于两个簇对称性,这些规则偏离了在时间上连续耦合的振子的已知规则。第三种对称性产生了异宿网络,它由所有具有这种对称性的不稳定吸引子以及它们之间的连接组成。我们的研究结果表明,脉冲耦合系统可以可靠地产生定义良好的复杂的时空模式,符合特定的过渡规则集。我们简要地讨论了可能的影响与尖峰神经系统的计算。

英文摘要:

Pulse-coupled systems such as spiking neural networks exhibit nontrivial invariant sets in the form of attracting yet unstable saddle periodic orbits where units are synchronized into groups. Heteroclinic connections between such orbits may in principle support switching processes in those networks and enable novel kinds of neural computations. For small networks of coupled oscillators we here investigate under which conditions and how system symmetry enforces or forbids certain switching transitions that may be induced by perturbations. For networks of five oscillators we derive explicit transition rules that for two cluster symmetries deviate from those known from oscillators coupled continuously in time. A third symmetry yields heteroclinic networks that consist of sets of all unstable attractors with that symmetry and the connections between them. Our results indicate that pulse-coupled systems can reliably generate well-defined sets of complex spatiotemporal patterns that conform to specific transition rules. We briefly discuss possible implications for computation with spiking neural systems.

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