论文标题
通过复发神经网络对随机射击序列的在线记忆
Online Memorization of Random Firing Sequences by a Recurrent Neural Network
论文作者
论文摘要
本文研究了复发性神经网络模型通过简单的本地学习规则记住随机动态射击模式的能力。考虑了两种学习/记忆模式：第一个模式严格地在线，单个通过数据，而第二种模式则使用多个通过数据。在这两种模式下，学习都是严格的本地（准赫比安）：在任何给定的时间步骤中，只有在上一个时间步骤的神经元射击（或应该要射击）与当前时间步骤的射击（或应该射击）之间的权重进行了修改。本文的主要结果是单次记忆并不完美的概率上的上限。因此，在这种模式下的记忆能力渐近地比例像经典的Hopfield模型（相比之下，它记住静态模式）。但是，多轮记忆显示可实现更高的能力（每个连接/突触的位数量不存在）。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。
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.
