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Phase Resetting Curves Allow for Simple and Accurate Prediction of Robust N:1 Phase Locking for Strongly Coupled Neural Oscillators

机译:相位复位曲线可对强耦合神经振荡器的鲁棒N:1锁相进行简单而准确的预测

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摘要

Existence and stability criteria for harmonic locking modes were derived for two reciprocally pulse coupled oscillators based on their first and second order phase resetting curves. Our theoretical methods are general in the sense that no assumptions about the strength of coupling, type of synaptic coupling, and model are made. These methods were then tested using two reciprocally inhibitory Wang and Buzsáki model neurons. The existence of bands of 2:1, 3:1, 4:1, and 5:1 phase locking in the relative frequency parameter space was predicted correctly, as was the phase of the slow neuron's spike within the cycle of the fast neuron in which it occurred. For weak coupling the bands are very narrow, but strong coupling broadens the bands. The predictions of the pulse coupled method agreed with weak coupling methods in the weak coupling regime, but extended predictability into the strong coupling regime. We show that our prediction method generalizes to pairs of neural oscillators coupled through excitatory synapses, and to networks of multiple oscillatory neurons. The main limitation of the method is the central assumption that the effect of each input dies out before the next input is received.
机译:根据两个互锁脉冲耦合振荡器的一阶和二阶相位复位曲线,推导了谐波锁定模式的存在性和稳定性标准。在没有对耦合强度,突触耦合类型和模型进行任何假设的意义上,我们的理论方法是通用的。然后使用两个相互抑制的Wang和Buzsáki模型神经元测试了这些方法。正确预测了相对频率参数空间中存在2:1、3:1、4:1和5:1锁相带,以及快速神经元周期中慢神经元尖峰的相位也是如此。它发生了。对于弱耦合,频带非常窄,但是强耦合会使频带变宽。脉冲耦合方法的预测与弱耦合方法中的弱耦合方法一致,但是将可预测性扩展到了强耦合方法中。我们表明,我们的预测方法可概括为通过兴奋性突触耦合的成对神经振荡器,以及多个振荡神经元的网络。该方法的主要局限性在于以下中心假设:每个输入的影响在接收下一个输入之前就消失了。

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