A study on processing device for information coding of a nervous system

神经系统信息编码处理装置的研究

基本信息

批准号：
11650355
负责人：
SEKINE Yoshifumi
金额：
$ 1.73万
依托单位：
Nihon University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1999
资助国家：
日本
起止时间：
1999 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-11650355/
关键词：
Neuron Model Neural Network Neural Coding Analog Circuit Pulse-type Axon Model Lambda-type Neuron Model Chaos Neuron Model 樹状突起モデル Λ形ニューロンモデル時系列パターン識別ネオコグニトロン八木型ニューロンモデルシミュレーションカオス

项目摘要

We have been studying mechanisms of neural coding, and we have been trying to produce hardware from the viewpoint that development of new hardware neuron devices is one of the important problems in the study of neural networks. Furthermore, we have been studying how to develop a hardware neural network for information processing systems. In this study, we discuss as follows :1. Investigate what happens neural coding in a nervous system.2. Develop new neuron devices.3. Develop temporal pattern recognition for neural network.Results, 1. (1) It was shown that the asynchronous chaotic neuron model with effects on membrane potential on post synaptic potential had absolute refractoriness (References No. 1).(2) The axon's output spike train displayed chaotic features when the chaotic spike train was transmitted by propagation along the active axon. Moreover, the time series intervals obtained from the axon's output spike train were an almost random train of the inter spike intervals (References No. 1).2. (1) It was shown that the axon model, which has a certain threshold with respect to the signal height, exhibits the all-or-none law in the same way as the traditional active line, and also displays chaotic phenomena (References No. 3).(2) We developed a pulse-type hardware bursting neuron device (References No. 4).(3) We constructed pulse-type hardware neuron devices for neural networks (References No. 6, 7).(4) We developed a pulse-type hardware bursting neuron device for IC implementation (References No. 9).(5) We realized an asynchronous chaotic neuron device using analog circuits (References No. 8).3. It was shown that by using a layered neural network, which has a structure suitable for a temporal nature, we investigated a new method of discriminating temporal patterns, such as EEG of a mouse. When we developed this neural network, we will be able to construct simple circuits, because there are not many connected lines among the neurons (References No. 5).

我们一直在研究神经编码的机制，我们一直在尝试从新硬件神经元设备的开发是神经网络研究中的重要问题之一。此外，我们一直在研究如何开发用于信息处理系统的硬件神经网络。在这项研究中，我们讨论如下：1。研究神经系统中的神经编码会发生什么。2。开发新的神经元设备3。为神经网络开发时间模式识别。（1）。此外，从轴突的输出尖峰列车获得的时间序列间隔几乎是间隔间隔的随机列车（参考文献1）。2。（1）表明，相对于信号高度具有一定阈值的轴突模型以与传统活动线相同的方式展示了无与伦比的法律，并且还显示了混乱现象（参考文献3）。（参考文献3）。（2）我们开发了脉冲型硬件硬件设备（参考Neuron neuron ne. 4）。 7）。（4）我们开发了用于IC实现的脉冲型硬件爆发神经元设备（参考号9）。（5）我们使用模拟电路实现了异步的混沌神经元设备（参考文献8）.3。结果表明，通过使用具有适用于时间性质的结构的分层神经网络，我们研究了一种新的分歧时间模式的方法，例如小鼠的脑电图。当我们开发此神经网络时，我们将能够构建简单的电路，因为神经元之间没有连接的线路（参考文献5）。