随机耦合超临界Andronov-Hopf振子系统中亚临界分岔的出现：神经网络类型转换的一种可能机制
Emergence of Subcritical Bifurcations in a System of Randomly Coupled Supercritical Andronov-Hopf Oscillators: A Potential Mechanism for Neural Network Type Switching
摘要：
实验证据表明，皮质系统的计算状态根据行为和刺激上下文而改变。然而，它仍然是未知的机制，这种适应性处理皮层电路。在本文中，我们提出了一个随机耦合的超临界Andronov-Hopf振荡器模型，它可以作为一个自适应处理器，表现出显着不同的动态依赖于一个单一的网络参数的值。尽管仅由超临界子单元组成，但整个系统可以表现出超临界或亚临界Andronov-Hopf分叉。该模型可能为模式识别理论中全局渐近稳定和非双曲神经网络类型之间的切换提供一种新的机制。
英文摘要：
Experimental evidence suggests that the computational state of cortical systems change according to behavioral and stimulus context. However, it is still unknown what mechanisms underlie this adaptive processing in cortical circuitry. In this paper, we present a model of randomly coupled supercritical Andronov-Hopf oscillators which can act as an adaptive processor by exhibiting drastically different dynamics depending on the value of a single network parameter. Despite being only composed of supercritical subunits, the full system can exhibit either supercritical or subcritical Andronov-Hopf bifurcations. This model might provide a novel mechanism for switching between globally asymptotically stable and nonhyperbolic neural network types in pattern recognition theory.
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