The Supervised Learning Rules of the Pulsed Neuron Model

我们提出了一个听觉神经网络模型，使用脉冲神经元模型（PNM）来处理以脉冲形式呈现的信息。在之前的研究中，PNM中使用的参数并不是自动配置的。因此，很难将该模型实现到实际应用中。这种限制成为建立脉冲神经元模型专用的通用算法的动机。在本研究的第一年，我们提出了两种适合基于PNM的实现的新算法。第一个是监督算法，用来学习脉冲之间的具体相位差。第二种是模式分类的无监督算法。通过实现这些算法，PNM具有非线性映射的能力，使其适合于实际领域的应用。第二年，对监督算法在声源定位问题和声源识别问题上的性能进行了评价。实验在使用真实声音数据库的条件下进行。在这些研究中，我们也试图在一个有各种声音的环境中评估系统。针对这种情况，在系统中加入了一个时间差提取模块作为预处理部分，对输入信号的声音进行分类。实验结果表明，该系统具有识别特定方向特定声音的能力。本研究的一部分还专门研究了PNM硬件实现的成本评估。我们提出了一种有效的方法来实现PNM作为逻辑电路，实验表明了预期的效果。本研究的进展分别在1999年12月、2001年3月的IEICE神经计算技术小组研讨会、1999年11月的国际神经信息处理会议和2000年7月的国际神经网络联合会议上发表。

We propose an Auditory Neural Network Model using the Pulsed Neuron Model (PNM) to deal with the information which is presented as pulse. In the previous progress of this study, the parameters used in PNM were not configured automatically. Therefore, it was difficult to implement the model into real applications. This limitation became the motivation to build a general-purpose algorithms dedicated for the Pulsed Neuron Model. In the first year of this study, we proposed 2 new algorithms which were suitable for PNM based implementation. The first one was supervised algorithm to learn the specific phase-difference between the pulses. The second one was an unsupervised algorithm for pattern classification. By implementing these algorithms, PNM had the capability of non-linear mapping to make it suitable for real-world domain application. In the second year, the performance of the supervised algorithm was evaluated on sound source localization problem and sound source recognition problem. The experiments were performed under the condition which was using real-life sound database. In these study, we also attempted to evaluate the system in an environment which comes with various sounds. To deal with this condition, a time-difference extraction module was added to the system as preprocessing part to classify the sound of the input signal. The experiment results showed that the system had the capability to recognize specific sound from specific direction. A part of this study was also dedicated on the cost evaluation for the hardware implementation of PNM. We proposed efficient method to implement PNM as logic circuit and the experiments showed promised result. The progress of this study was presented in IEICE Neurocomputing-Technical Group Workshop on December 1999, March 2001, International Conference On Neural Information Processing '99 (November 1999) and International Joint Conference on Neural Network 2000 (July 2000).