Dept. of Computer Sciences and Information Engineering, Southern Taiwan University, Tainan, Taiwan.
Brain Res. 2012 Jan 24;1434:90-101. doi: 10.1016/j.brainres.2011.09.042. Epub 2011 Sep 29.
Frequency modulation (FM) is an important building block of communication signals for animals and human. Attempts to predict the response of central neurons to FM sounds have not been very successful, though achieving successful results could bring insights regarding the underlying neural mechanisms. Here we proposed a new method to predict responses of FM-sensitive neurons in the auditory midbrain. First we recorded single unit responses in anesthetized rats using a random FM tone to construct their spectro-temporal receptive fields (STRFs). Training of neurons in the artificial neural network to respond to a second random FM tone was based on the temporal information derived from the STRF. Specifically, the time window covered by the presumed trigger feature and its delay time to spike occurrence were used to train a finite impulse response neural network (FIRNN) to respond to this random FM. Finally we tested the model performance in predicting the response to another similar FM stimuli (third random FM tone). We found good performance in predicting the time of responses if not also the response magnitudes. Furthermore, the weighting function of the FIRNN showed temporal 'bumps' suggesting temporal integration of synaptic inputs from different frequency laminae. This article is part of a Special Issue entitled: Neural Coding.
调频（FM）是动物和人类通讯信号的重要组成部分。尽管取得成功的结果可能会带来关于潜在神经机制的见解，但尝试预测中枢神经元对 FM 声音的反应一直不是很成功。在这里，我们提出了一种新的方法来预测听觉中脑 FM 敏感神经元的反应。首先，我们使用随机 FM 音调在麻醉大鼠中记录单个单元的反应，以构建它们的频谱-时变接受域（STRF）。基于从 STRF 中得出的时间信息，对人工神经网络中的神经元进行训练以响应第二个随机 FM 音调。具体来说，使用假定的触发特征及其延迟时间到尖峰发生的时间窗口来训练有限脉冲响应神经网络（FIRNN）以响应此随机 FM。最后，我们测试了模型在预测另一个类似 FM 刺激（第三个随机 FM 音调）时的响应的性能。如果不是响应幅度，我们发现对响应时间的预测性能良好。此外，FIRNN 的加权函数显示出时间上的“凸起”，表明来自不同频率层的突触输入的时间整合。本文是特刊“神经编码”的一部分。
