通过随机神经网络中不同的活动模式对周期性定时动作电位的频率解码
Frequency decoding of periodically timed action potentials through distinct activity patterns in a random neural network
摘要：
频率辨别是听觉系统的一项基本任务。哺乳动物的内耳或耳蜗提供了一个位置代码，其中在不同的空间位置检测到不同的频率。然而，基于尖峰时间的时间代码也是可用的：低频音调在神经纤维中诱发的动作电位发生在刺激的优选相位-它们表现出相位锁定-因此提供了关于音调频率的时间信息。在伴随的心理声学研究，并与以前的实验一致，我们表明，人类采用这种时间信息的歧视的低频。这些时间信息是如何在大脑中读出的呢？在这里，我们证明了经常性的随机神经网络，其中神经元之间的连接引入特征时间延迟，神经元需要时间上一致的输入尖峰启动，可以执行尖锐的频率歧视时，锁相输入刺激。虽然这种网络实现的频率分辨率受到锁相噪声的限制，但实际值的分辨率达到了在人体中测量到的0.2%的微小频率差。
英文摘要：
Frequency discrimination is a fundamental task of the auditory system. The mammalian inner ear, or cochlea, provides a place code in which different frequencies are detected at different spatial locations. However, a temporal code based on spike timing is also available: action potentials evoked in an auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the stimulus-they exhibit phase locking-and thus provide temporal information about the tone's frequency. In an accompanying psychoacoustic study, and in agreement with previous experiments, we show that humans employ this temporal information for discrimination of low frequencies. How might such temporal information be read out in the brain? Here we demonstrate that recurrent random neural networks in which connections between neurons introduce characteristic time delays, and in which neurons require temporally coinciding inputs for spike initiation, can perform sharp frequency discrimination when stimulated with phase-locked inputs. Although the frequency resolution achieved by such networks is limited by the noise in phase locking, the resolution for realistic values reaches the tiny frequency difference of 0.2% that has been measured in humans.
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