Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970, Brazil.
Departamento de Física, Universidade Federal de Pernambuco, Recife, Pernambuco 50670-901, Brazil.
Phys Rev E. 2017 May;95(5-1):052410. doi: 10.1103/PhysRevE.95.052410. Epub 2017 May 22.
Anticipated synchronization (AS) is a counterintuitive behavior that has been observed in several systems. When AS occurs in a sender-receiver configuration, the latter can predict the future dynamics of the former for certain parameter values. In particular, in neuroscience AS was proposed to explain the apparent discrepancy between information flow and time lag in the cortical activity recorded in monkeys. Despite its success, a clear understanding of the mechanisms yielding AS in neuronal circuits is still missing. Here we use the well-known phase-response-curve (PRC) approach to study the prototypical sender-receiver-interneuron neuronal motif. Our aim is to better understand how the transitions between delayed to anticipated synchronization and anticipated synchronization to phase-drift regimes occur. We construct a map based on the PRC method to predict the phase-locking regimes and their stability. We find that a PRC function of two variables, accounting simultaneously for the inputs from sender and interneuron into the receiver, is essential to reproduce the numerical results obtained using a Hodgkin-Huxley model for the neurons. On the contrary, the typical approximation that considers a sum of two independent single-variable PRCs fails for intermediate to high values of the inhibitory coupling strength of the interneuron. In particular, it loses the delayed-synchronization to anticipated-synchronization transition.
预期同步 (AS) 是一种在多个系统中观察到的反直觉行为。当 AS 发生在发送方-接收方配置中时，后者可以预测前者在某些参数值下的未来动力学。特别是，在神经科学中，AS 被提议用来解释在猴子记录的皮质活动中信息流和时间滞后之间明显的差异。尽管取得了成功，但对于产生神经元电路中 AS 的机制仍缺乏清晰的理解。在这里，我们使用众所周知的相位响应曲线 (PRC) 方法来研究原型发送方-接收方-中间神经元神经元模式。我们的目的是更好地理解延迟同步到预期同步和预期同步到相位漂移状态的转变是如何发生的。我们构建了一个基于 PRC 方法的映射来预测锁相状态及其稳定性。我们发现，同时考虑发送器和中间神经元输入到接收器的双变量 PRC 函数对于重现使用 Hodgkin-Huxley 模型对神经元进行数值模拟的结果是必不可少的。相反，对于中间神经元的抑制耦合强度处于中等至高值时，典型的近似方法，即考虑两个独立的单变量 PRC 的和，会失效。特别是，它失去了从延迟同步到预期同步的转变。
