权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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，并且从开发新的硬件神经元器件是神经网络研究中的重要问题之一的观点出发，我们一直在尝试生产硬件。此外，我们一直在研究如何开发一个硬件神经网络的信息处理系统。在本研究中，我们讨论如下：1。研究神经系统中发生的神经编码。2.开发新的神经元器件。发展神经网路之时间模式辨识。(1)结果表明，考虑膜电位对突触后电位影响的异步混沌神经元模型具有绝对不应性（参考文献1）。(2)当神经元的输出锋电位序列通过沿着活动轴突传播时，轴突的输出锋电位序列呈现混沌特征。此外，从轴突的输出锋电位序列获得的时间序列间隔是一个几乎随机的锋电位间隔序列（参考文献1）。(1)结果表明，轴突模型，它有一个特定的阈值，相对于信号的高度，表现出全有或全无的法律，以同样的方式，传统的活动线，也显示混沌现象（参考文献3）。(2)我们开发了一种脉冲型硬件爆发神经元装置（参考文献4）。(3)我们构建了用于神经网络的脉冲型硬件神经元器件（参考文献6、7）。(4)我们开发了一种用于IC实现的脉冲型硬件突发神经元器件（参考文献9）。(5)利用模拟电路实现了一个异步混沌神经元器件（参考文献8）.结果表明，通过使用一个分层的神经网络，它具有适合的时间性质的结构，我们研究了一种新的方法来区分时间模式，如小鼠的EEG。当我们开发这个神经网络时，我们将能够构建简单的电路，因为神经元之间没有太多的连接线（参考文献5）。