论文标题
工作记忆的免费回忆动力的稳态行为
Steady state behavior of the free recall dynamics of working memory
论文作者
论文摘要
本文研究了一个动态系统，该系统模拟了工作记忆的自由回忆动力学。该模型是一个模块化神经网络，该网络具有n个模块，名为HyperColumns，每个模块由M微型室组成。在微小级别之间的连接权重的轻度条件下，我们研究了模型的长期演化行为，即平衡的存在和稳定性和限制周期。我们还赋予了HOPF分叉发生的临界价值。最后，我们提供了足够的条件，在该条件下，该模型在每个高柱中具有与同步的微型级状态具有全球渐近稳定的平衡，这意味着在这种情况下，召回是不可能的。提供数值模拟以说明我们的理论结果。我们给出的一个数字示例表明，模式不仅可以存储在平衡和限制周期中，而且还可以限制奇怪的吸引子（或混乱）。
This paper studies a dynamical system that models the free recall dynamics of working memory. This model is a modular neural network with n modules, named hypercolumns, and each module consists of m minicolumns. Under mild conditions on the connection weights between minicolumns, we investigate the long-term evolution behavior of the model, namely the existence and stability of equilibriums and limit cycles. We also give a critical value in which Hopf bifurcation happens. Finally, we give a sufficient condition under which this model has a globally asymptotically stable equilibrium with synchronized minicolumn states in each hypercolumn, which implies that in this case recalling is impossible. Numerical simulations are provided to illustrate our theoretical results. A numerical example we give suggests that patterns can be stored in not only equilibriums and limit cycles, but also strange attractors (or chaos).
