Networks of spiking neurons can have persistently firing stable bump attractors to represent continuous spaces (like temperature). This can be done with a topology with local excitatory synapses and local surround inhibitory synapses. Activating large ranges in the attractor can lead to multiple bumps, that show repeller and attractor dynamics; however, these bumps can be merged by overcoming the repeller dynamics. A simple associative memory can include these bump attractors, allowing the use of continuous variables in these memories, and these associations can be learned by Hebbian rules. These simulations are related to biological networks, showing that this is a step toward a more complete neural cognitive associative memory.

中文翻译：

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热咖啡：联想记忆与尖峰神经元的碰撞吸引细胞组件。

尖峰神经元网络可以持续发射稳定的凹凸吸引子来表示连续空间（如温度）。这可以通过具有局部兴奋性突触和局部环绕抑制性突触的拓扑来完成。激活吸引子中的大范围会导致多个颠簸，显示排斥器和吸引子动力学；然而，这些颠簸可以通过克服排斥动力学来合并。一个简单的联想记忆可以包括这些凹凸吸引子，允许在这些记忆中使用连续变量，这些关联可以通过赫布规则学习。这些模拟与生物网络有关，表明这是朝着更完整的神经认知联想记忆迈出的一步。