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Existence and stability criteria for phase-locked modes in ring neural networks based on the spike time resetting curve method.

机译:基于尖峰时间重置曲线方法的环形神经网络锁相模态的存在性和稳定性判据。

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We developed a systematic and consistent mathematical approach to predicting 1:1 phase-locked modes in ring neural networks of spiking neurons based on the open loop spike time resetting curve (STRC) and its almost equivalent counterpart-the phase resetting curve (PRC). The open loop STRCs/PRCs were obtained by injecting into an isolated model neuron a triangular shaped time-dependent stimulus current closely resembling an actual synaptic input. Among other advantages, the STRC eliminates the confusion regarding the undefined phase for stimuli driving the neuron outside of the unperturbed limit cycle. We derived both open loop PRC and STRC-based existence and stability criteria for 1:1 phase-locked modes developed in ring networks of spiking neurons. Our predictions were in good agreement with the closed loop numerical simulations. Intuitive graphical methods for predicting phase-locked modes were also developed both for half-centers and for larger ring networks.
机译:我们基于开环尖峰时间重置曲线(STRC)及其几乎等效的相位重置曲线(PRC),开发了一种系统一致的数学方法来预测尖峰神经元的环形神经网络中的1:1锁相模式。开环STRC / PRC是通过将与实际突触输入非常相似的三角形时间相关刺激电流注入隔离的模型神经元中而获得的。除其他优点外,STRC消除了有关不确定阶段的困惑,该不确定阶段用于在不受限制的极限周期之外刺激神经元。我们得出了尖峰神经元环网络中开发的基于1:1锁相模态的开环PRC和基于STRC的存在和稳定性标准。我们的预测与闭环数值模拟非常吻合。还针对半中心和较大的环形网络开发了用于预测锁相模式的直观图形方法。

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