论文标题
塑料全身抑制控制幅度，同时允许在随机神经场模型中进行相模。
Plastic systemic inhibition controls amplitude while allowing phase pattern in a stochastic neural field model
论文作者
论文摘要
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模型的振荡相模式形成和振幅控制。发现，对于几种参数选择，当且仅当允许幅度较大时，就会发生这些过程阶段的模式形成。受到稳态抑制性可塑性的最新研究的刺激，我们引入了静态和塑性（自适应）全身抑制机制，以使振幅保持随后的模拟在随机模拟中保持稳定。具有静态全身性抑制的系统表现出有界的幅度，但没有持续的相模式，而具有塑性全身抑制的系统既显示出有界的幅度和持续的相模式。这些结果表明，神经场模型中的塑性抑制机制可以随机控制幅度，同时允许相同步的模式发展。塑性全身抑制的类似机制可能在调节大脑中的振荡功能方面发挥作用。
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.
