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Using Phase Resetting to Predict 1:1 and 2:2 Locking in Two Neuron Networks in Which Firing Order is not Always Preserved

机译:在不总是保持点火顺序的两个神经元网络中使用相位重置来预测1:1和2:2锁定

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摘要

Our goal is to understand how nearly synchronous modes arise in heterogenous networks of neurons. In heterogenous networks, instead of exact synchrony, nearly synchronous modes arise, which include both 1:1 and 2:2 phase-locked modes. Existence and stability criteria for 2:2 phase-locked modes in reciprocally coupled two neuron circuits were derived based on the open loop phase resetting curve (PRC) without the assumption of weak coupling. The PRC for each component neuron was generated using the change in synaptic conductance produced by a presynaptic action potential as the perturbation. Separate derivations were required for modes in which the firing order is preserved and for those in which it alternates. Networks composed of two model neurons coupled by reciprocal inhibition were examined to test the predictions. The parameter regimes in which both types of nearly synchronous modes are exhibited were accurately predicted both qualitatively and quantitatively provided that the synaptic time constant is short with respect to the period and that the effect of second order resetting is considered. In contrast, PRC methods based on weak coupling could not predict 2:2 modes and did not predict the 1:1 modes with the level of accuracy achieved by the strong coupling methods. The strong coupling prediction methods provide insight into what manipulations promote near-synchrony in a two neuron network and may also have predictive value for larger networks, which can also manifest changes in firing order. We also identify a novel route by which synchrony is lost in mildly heterogenous networks.
机译:我们的目标是了解在异质神经元网络中几乎如何出现同步模式。在异构网络中,不是完全同步,而是出现了几乎同步的模式,其中包括1:1和2:2锁相模式。基于开环相位复位曲线(PRC),在不考虑弱耦合的情况下,得出了两个耦合的神经元回路中2:2锁相模的存在性和稳定性标准。使用由突触前动作电位产生的突触电导的变化作为扰动来生成每个组成神经元的PRC。对于保留点火顺序的模式和交替交替的模式,需要单独的推导。检查了由两个相互抑制的模型神经元组成的网络,以检验预测。只要突触时间常数相对于周期短并且考虑到二阶重置的影响,就可以定性和定量地准确预测出同时出现两种类型的几乎同步模式的参数方案。相比之下,基于弱耦合的PRC方法无法预测2:2模式,也无法预测1:1模式,而强耦合方法所达到的准确度水平。强大的耦合预测方法可洞悉哪些操作促进了两个神经元网络中的近同步,并且对于较大的网络也可能具有预测价值,该网络还可以显示触发顺序的变化。我们还确定了一条轻度异构网络中失去同步性的新颖途径。

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