Neural oscillations, including rhythms in the beta1 band (12–20 Hz), are important in various cognitive functions. Often neural networks receive rhythmic input at frequencies different from their natural frequency, but very little is known about how such input affects the network’s behavior. We use a simplified, yet biophysical, model of a beta1 rhythm that occurs in the parietal cortex, in order to study its response to oscillatory inputs. We demonstrate that a cell has the ability to respond at the same time to two periodic stimuli of unrelated frequencies, firing in phase with one, but with a mean firing rate equal to that of the other. We show that this is a very general phenomenon, independent of the model used. We next show numerically that the behavior of a different cell, which is modeled as a high-dimensional dynamical system, can be described in a surprisingly simple way, owing to a reset that occurs in the state space when the cell fires. The interaction of the two cells leads to novel combinations of properties for neural dynamics, such as mode-locking to an input without phase-locking to it.

中文翻译：

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神经元生物物理网络中多个节律的相互作用

神经振荡，包括beta1波段（12–20 Hz）中的节律，在各种认知功能中都很重要。通常，神经网络以不同于其自然频率的频率接收有节奏的输入，但是对于这种输入如何影响网络行为的了解甚少。我们使用在顶叶皮层中发生的beta1节律的简化的但生物物理的模型，以研究其对振荡输入的响应。我们证明了一个细胞有能力同时响应两个不相关频率的周期性刺激，与一个同相触发，但平均发射速率等于另一个。我们证明这是一个非常普遍的现象，与所使用的模型无关。接下来，我们通过数字显示不同的单元格的行为，该单元格被建模为高维动力系统，由于单元触发时在状态空间中发生的重置，因此可以用令人惊讶的简单方式来描述。这两个单元的相互作用导致了神经动力学特性的新颖组合，例如将模式锁定到输入而没有将其锁相。