可塑性系统抑制控制振幅，同时允许随机神经场模型中的相位模式
Plastic systemic inhibition controls amplitude while allowing phase pattern in a stochastic neural field model
摘要：
通过模拟由随机微分方程定义的耦合随机过程，研究了线性化随机神经元场模型的振荡相位图案形成和振幅控制。结果发现，对于几个参数的选择，当且仅当允许振幅变大时，在这些过程的阶段中的图案形成才发生。受最近的稳态抑制可塑性工作的刺激，我们引入了静态和塑料（自适应）系统抑制机制，以保持振幅stochemical界在随后的模拟。静态系统抑制的系统表现出有界的幅度，但没有持续的相位模式，而与塑性系统抑制的系统表现出有界的幅度和持续的相位模式。这些结果表明，在神经场模型中的塑性抑制机制可以随机控制振幅，同时允许相位同步模式的发展。类似的可塑性系统抑制机制可能在调节大脑的振荡功能中发挥作用。
英文摘要：
Oscillatory phase pattern formation and amplitude control for a linearized stochastic neuron field model was investigated by simulating coupled stochastic processes defined by stochastic differential equations. It was found, for several choices of parameters, that pattern formation in the phases of these processes occurred if and only if the amplitudes were allowed to grow large. Stimulated by recent work on homeostatic inhibitory plasticity, we introduced static and plastic (adaptive) systemic inhibitory mechanisms to keep the amplitudes stochastically bounded in subsequent simulations. The systems with static systemic inhibition exhibited bounded amplitudes but no sustained phase patterns, whereas the systems with plastic systemic inhibition exhibited both bounded amplitudes and sustained phase patterns. These results demonstrate that plastic inhibitory mechanisms in neural field models can stochastically control amplitudes while allowing patterns of phase synchronization to develop. Similar mechanisms of plastic systemic inhibition could play a role in regulating oscillatory functioning in the brain.
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