随机放电序列的循环神经网络在线识别
Online Memorization of Random Firing Sequences by a Recurrent Neural Network
摘要：
研究了循环神经网络模型通过简单的局部学习规则记忆随机动态放电模式的能力。考虑两种学习/记忆模式：第一种模式是严格在线的，通过数据进行单次传递，而第二种模式使用多次传递数据。在这两种模式下，学习都是严格局部的（准赫布）：在任何给定的时间步，只有在前一个时间步发射（或假设发射）的神经元和在当前时间步发射（或假设发射）的神经元之间的权重被修改。本文的主要结果是上界的概率，单遍记忆是不完美的。由此可见，这种模式下的记忆能力与经典Hopfield模型（相反，它记忆静态模式）的记忆能力类似。然而，多轮记忆被证明可以实现更高的容量（每个连接/突触的位数不为零）。这些数学发现可能有助于理解短期记忆和长期记忆在神经科学中的功能。
英文摘要：
This paper studies the capability of a recurrent neural network model to memorize random dynamical firing patterns by a simple local learning rule. Two modes of learning/memorization are considered: The first mode is strictly online, with a single pass through the data, while the second mode uses multiple passes through the data. In both modes, the learning is strictly local (quasi-Hebbian): At any given time step, only the weights between the neurons firing (or supposed to be firing) at the previous time step and those firing (or supposed to be firing) at the present time step are modified. The main result of the paper is an upper bound on the probability that the single-pass memorization is not perfect. It follows that the memorization capacity in this mode asymptotically scales like that of the classical Hopfield model (which, in contrast, memorizes static patterns). However, multiple-rounds memorization is shown to achieve a higher capacity (with a nonvanishing number of bits per connection/synapse). These mathematical findings may be helpful for understanding the functions of short-term memory and long-term memory in neuroscience.
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。
