二维连续吸引子上的自发运动
Spontaneous Motion on Two-dimensional Continuous Attractors
摘要：
吸引子模型是用于描述神经元池的放电率曲线的动力学的简化模型。放电率曲线或神经元活动被认为携带信息。连续吸引子神经网络（CANNs）描述了连续信息的神经处理，如对象的位置，对象的方向和对象的运动方向。最近，人们发现，在一维CANN，短期突触抑制可以不稳定的隆起形神经元吸引子活动配置文件。在本文中，我们研究了具有短期突触抑制和锋电位频率适应的二维CANN。我们发现，CANNs与短期突触抑制和CANNs与尖峰频率适应的动力学性质相似。我们还发现，在这两种CANN的微扰方法可以用来预测相图，动力学变量和自发运动的速度。
英文摘要：
Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neuronal activity, is thought to carry information. Continuous attractor neural networks (CANNs) describe the neural processing of continuous information such as object position, object orientation and direction of object motion. Recently, it was found that, in one-dimensional CANNs, short-term synaptic depression can destabilize bump-shaped neuronal attractor activity profiles. In this paper, we study two-dimensional CANNs with short-term synaptic depression and with spike frequency adaptation. We found that the dynamics of CANNs with short-term synaptic depression and CANNs with spike frequency adaptation are qualitatively similar. We also found that in both kinds of CANNs the perturbative approach can be used to predict phase diagrams, dynamical variables and speed of spontaneous motion.
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