论文标题
峰值神经元的碰撞吸引力网络中的关键限制
Critical Limits in a Bump Attractor Network of Spiking Neurons
论文作者
论文摘要
凸起吸引子网络是一种实现从与输入源相关的尖峰模式出现的竞争神经元过程的模型。由于BUMP网络可以在许多方面行事，因此本文使用各种正面和负重探索了参数空间的一些关键限制，并且输入尖峰源的尺寸增加了BumpAttractor网络的神经形态模拟，表明它表现出固定的，splatister，splitting，splitting，splitting和divergent Spike模式，相对于不同的权重和Input Windows和Input Windows和Input Windeps和Input Windep。正重量和负权重值之间的平衡对于确定尖峰列车模式的分裂或分化行为以及定义最小的射击条件很重要。
A bump attractor network is a model that implements a competitive neuronal process emerging from a spike pattern related to an input source. Since the bump network could behave in many ways, this paper explores some critical limits of the parameter space using various positive and negative weights and an increasing size of the input spike sources The neuromorphic simulation of the bumpattractor network shows that it exhibits a stationary, a splitting and a divergent spike pattern, in relation to different sets of weights and input windows. The balance between the values of positive and negative weights is important in determining the splitting or diverging behaviour of the spike train pattern and in defining the minimal firing conditions.
