Cavendish Laboratory, University of Cambridge, Cambridge CB2 3RF, U.K.
Neural Comput. 2012 Aug;24(8):2053-77. doi: 10.1162/NECO_a_00306. Epub 2012 Apr 17.
We present a neural network that is capable of completing and correcting a spiking pattern given only a partial, noisy version. It operates in continuous time and represents information using the relative timing of individual spikes. The network is capable of correcting and recalling multiple patterns simultaneously. We analyze the network's performance in terms of information recall. We explore two measures of the capacity of the network: one that values the accurate recall of individual spike times and another that values only the presence or absence of complete patterns. Both measures of information are found to scale linearly in both the number of neurons and the period of the patterns, suggesting these are natural measures of network information. We show a smooth transition from encodings that provide precise spike times to flexible encodings that can encode many scenes. This makes it plausible that many diverse tasks could be learned with such an encoding.
我们提出了一个神经网络，它仅在接收到部分、嘈杂的版本时，就能够完成和纠正一个尖峰模式。它在连续时间内运行，并使用单个尖峰的相对时间来表示信息。该网络能够同时纠正和回忆多个模式。我们根据信息回忆来分析网络的性能。我们探索了两种衡量网络容量的方法：一种方法重视个别尖峰时间的准确回忆，另一种方法仅重视完整模式的存在或不存在。这两种信息度量都被发现与神经元的数量和模式的周期呈线性关系，这表明这些是网络信息的自然度量。我们展示了从提供精确尖峰时间的编码到可以编码许多场景的灵活编码的平滑转换。这使得使用这种编码学习许多不同的任务成为可能。
