Center for Molecular and Behavioral Neuroscience, Rutgers State University, Newark, NJ 07102, USA.
Center for Molecular and Behavioral Neuroscience, Rutgers State University, Newark, NJ 07102, USA.
Neuron. 2019 Feb 20;101(4):603-614.e6. doi: 10.1016/j.neuron.2018.12.028. Epub 2019 Jan 21.
A principle of communication technology, frequency mixing, describes how novel oscillations are generated when rhythmic inputs converge on a nonlinearly activating target. As expected given that neurons are nonlinear integrators, it was demonstrated that neuronal networks exhibit mixing in response to imposed oscillations of known frequencies. However, determining when mixing occurs in spontaneous conditions, where weaker or more variable rhythms prevail, has remained impractical. Here, we show that, by exploiting the predicted phase (rather than frequency) relationships between oscillations, the contributions of mixing can be readily identified, even in small samples of noisy data. Assessment of extracellular data using this approach revealed that frequency mixing is widely expressed in a state- and region-dependent manner and that oscillations emerging from mixing entrain unit activity. Frequency mixing is thus intrinsic to the structure of neural activity and contributes importantly to neural dynamics.
通信技术中的一个原理，频率混合，描述了当节奏输入汇聚到非线性激活的目标时如何产生新的振荡。由于神经元是非线性积分器，因此可以预期，神经元网络会对已知频率的强制振荡表现出混合。然而，在自发条件下确定混合是否发生，在这种情况下，较弱或更可变的节奏占主导地位，这一直是不切实际的。在这里，我们表明，通过利用振荡之间预测的相位（而不是频率）关系，可以很容易地识别混合的贡献，即使在噪声数据的小样本中也是如此。使用这种方法评估细胞外数据表明，频率混合以状态和区域依赖的方式广泛表达，并且混合产生的振荡使单位活动同步。因此，频率混合是神经活动结构的固有特性，并对神经动力学有重要贡献。
