Maran Selva K, Canavier Carmen C
Neuroscience Center for Excellence, LSU Health Sciences Center, New Orleans, LA 70112, USA.
J Comput Neurosci. 2008 Feb;24(1):37-55. doi: 10.1007/s10827-007-0040-z. Epub 2007 Jun 19.
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.
我们的目标是了解在神经元异质网络中近乎同步的模式是如何产生的。在异质网络中,出现的不是精确同步,而是近乎同步的模式,其中包括1:1和2:2锁相模式。基于开环相位重置曲线(PRC),在不假设弱耦合的情况下,推导了相互耦合的双神经元回路中2:2锁相模式的存在性和稳定性标准。每个组成神经元的PRC是使用由突触前动作电位产生的突触电导变化作为扰动来生成的。对于放电顺序保持不变的模式和放电顺序交替的模式,需要分别进行推导。研究了由相互抑制耦合的两个模型神经元组成的网络,以检验这些预测。只要突触时间常数相对于周期较短并且考虑二阶重置的影响,就能在定性和定量上准确预测同时呈现两种近乎同步模式的参数范围。相比之下,基于弱耦合的PRC方法无法预测2:2模式,并且无法以强耦合方法所达到的精度预测1:1模式。强耦合预测方法为促进双神经元网络中近乎同步的操作提供了见解,并且对于更大的网络可能也具有预测价值,更大的网络也可能表现出放电顺序的变化。我们还确定了一种新的途径,通过该途径,轻度异质网络中的同步性会丧失。