发展神经元竞争生存的神经网络

Zhen Peng, Daniel A. Braun
{"title":"发展神经元竞争生存的神经网络","authors":"Zhen Peng, Daniel A. Braun","doi":"10.1109/DEVLRN.2015.7346133","DOIUrl":null,"url":null,"abstract":"We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. If a neuron has found a stable firing pattern, a new neuron is created in the same layer. It is also made sure that there is always at least one neuron in each activated layer that is searching for novel patterns. If a layer stops growing for a certain amount of time, a new layer is created starting with a single neuron.","PeriodicalId":164756,"journal":{"name":"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Developing neural networks with neurons competing for survival\",\"authors\":\"Zhen Peng, Daniel A. Braun\",\"doi\":\"10.1109/DEVLRN.2015.7346133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. If a neuron has found a stable firing pattern, a new neuron is created in the same layer. It is also made sure that there is always at least one neuron in each activated layer that is searching for novel patterns. If a layer stops growing for a certain amount of time, a new layer is created starting with a single neuron.\",\"PeriodicalId\":164756,\"journal\":{\"name\":\"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DEVLRN.2015.7346133\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEVLRN.2015.7346133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

我们研究发育生长的前馈神经网络模型的灵感来自自然界的生存原则。每个神经元都必须以一种允许其放电的方式选择进入的连接,因为在一段时间内不能放电的神经元会退化并死亡。为了存活,神经元必须在前一层神经元的活动中找到重复出现的模式,因为每个神经元在任何时候都需要不止一个活跃输入来获得足够的激活。因此,最低层的感觉输入提供了所有神经元竞争的最大激活量。整个网络随着时间的推移动态增长,这取决于可以找到多少模式,以及有多少神经元可以相应地维持自己。如果一个神经元找到了一个稳定的放电模式,在同一层就会产生一个新的神经元。它还确保在每个激活层中至少有一个神经元在寻找新的模式。如果一个神经元层停止生长一段时间,就会从一个神经元开始形成一个新的神经元层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing neural networks with neurons competing for survival
We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. If a neuron has found a stable firing pattern, a new neuron is created in the same layer. It is also made sure that there is always at least one neuron in each activated layer that is searching for novel patterns. If a layer stops growing for a certain amount of time, a new layer is created starting with a single neuron.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信