神经网络的随机元胞自动机模型
Stochastic cellular automata model of neural networks
摘要：
我们提出了一个随机动力学模型的噪声神经网络与复杂的架构，并讨论激活的神经网络的刺激，起搏器和自发活动。该模型具有复杂的相图，具有自组织的活跃神经状态、混合相变和丰富的行为阵列。我们表明，如果自发活动（噪声）达到阈值水平，那么全球神经振荡出现。随机共振是这种动力学相变的前兆。这些振荡是即使是50个神经元的小群体的内在属性。
英文摘要：
We propose a stochastic dynamical model of noisy neural networks with complex architectures and discuss activation of neural networks by a stimulus, pacemakers and spontaneous activity. This model has a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of behavior. We show that if spontaneous activity (noise) reaches a threshold level then global neural oscillations emerge. Stochastic resonance is a precursor of this dynamical phase transition. These oscillations are an intrinsic property of even small groups of 50 neurons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
