Fung C C Alan, Amari S-i
Department of Physics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
Neural Comput. 2015 Mar;27(3):507-47. doi: 10.1162/NECO_a_00711. Epub 2015 Jan 20.
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 article, we study two-dimensional CANNs with short-term synaptic depression and 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.
吸引子模型是用于描述一组神经元放电率分布动态的简化模型。放电率分布，即神经元活动，被认为携带信息。连续吸引子神经网络（CANNs）描述了诸如物体位置、物体方向和物体运动方向等连续信息的神经处理过程。最近发现，在一维CANNs中，短期突触抑制会使凸起状的神经元吸引子活动分布变得不稳定。在本文中，我们研究了具有短期突触抑制和放电频率适应的二维CANNs。我们发现，具有短期突触抑制的CANNs和具有放电频率适应的CANNs的动态在定性上是相似的。我们还发现，在这两种CANNs中，微扰方法可用于预测相图、动态变量和自发运动速度。
