Wang Junjie, Shen Jianwei
School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, China.
School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou, China.
Front Psychiatry. 2023 Mar 27;14:1083015. doi: 10.3389/fpsyt.2023.1083015. eCollection 2023.
The study of brain function has been favored by scientists, but the mechanism of short-term memory formation has yet to be precise.
Since the formation of short-term memories depends on neuronal activity, we try to explain the mechanism from the neuron level in this paper.
Due to the modular structures of the brain, we analyze the pattern properties of the FitzHugh-Nagumo model (FHN) on a multilayer network (coupled by a random network). The conditions of short-term memory formation in the multilayer FHN model are obtained. Then the time delay is introduced to more closely match patterns of brain activity. The properties of periodic solutions are obtained by the central manifold theorem.
When the diffusion coeffcient, noise intensity , and network connection probability reach a specific range, the brain forms a relatively vague memory. It is found that network and time delay can induce complex cluster dynamics. And the synchrony increases with the increase of . That is, short-term memory becomes clearer.
脑功能的研究一直受到科学家们的青睐，但短期记忆形成的机制尚未明确。
由于短期记忆的形成依赖于神经元活动，本文我们试图从神经元层面解释其机制。
鉴于大脑的模块化结构，我们分析了多层网络（由随机网络耦合）上的菲茨休 - 纳古莫模型（FHN）的模式特性。得到了多层FHN模型中短期记忆形成的条件。然后引入时间延迟以更紧密地匹配大脑活动模式。通过中心流形定理获得周期解的性质。
当扩散系数、噪声强度和网络连接概率达到特定范围时，大脑形成相对模糊的记忆。发现网络和时间延迟可诱导复杂的簇动力学。并且同步性随着[此处原文缺失某个变量的描述，可能有误]的增加而增加。即短期记忆变得更清晰。
