连续时间神经网络能够稳定记忆随机脉冲序列
Continuous-Time Neural Networks Can Stably Memorize Random Spike Trains
AI总结：
本文通过数值实验证明，连续时间循环神经网络可借助离线计算的突触权重，以接近一的概率稳定记忆并自主再现随机脉冲序列，并支持噪声下的联想回忆。
摘要：
本文探讨了连续时间循环神经网络存储和回忆精确定时的脉冲序列组的能力。我们通过数值实验表明，这确实是可能的：在一定参数范围内，任意随机脉冲序列组（针对网络中的所有神经元）都能被鲁棒地记忆，并以接近一的概率自主再现，且所有脉冲具有稳定、精确的相对时序。我们还演示了噪声条件下的联想回忆。在这些实验中，所需的突触权重是离线计算的，以满足一个鼓励时间稳定性的模板。
英文摘要：
The paper explores the capability of continuous-time recurrent neural networks to store and recall precisely timed scores of spike trains. We show (by numerical experiments) that this is indeed possible: within some range of parameters, any random score of spike trains (for all neurons in the network) can be robustly memorized and autonomously reproduced with stable accurate relative timing of all spikes, with probability close to one. We also demonstrate associative recall under noisy conditions. In these experiments, the required synaptic weights are computed offline, to satisfy a template that encourages temporal stability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
