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.

中文翻译：

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尖峰神经元凹凸吸引子网络的关键限制

凹凸吸引器网络是一种模型，它实现了从与输入源相关的尖峰模式中出现的竞争性神经元过程。由于凹凸网络可以以多种方式运行，本文使用各种正负权重以及输入尖峰源的不断增加的大小来探索参数空间的一些关键限制。bumpattractor 网络的神经形态模拟表明它表现出平稳的、分裂和发散尖峰模式，与不同的权重和输入窗口集有关。正负权重值之间的平衡对于确定尖峰序列模式的分裂或发散行为以及定义最小点火条件很重要。