A study of brains complex organs-organisms with artificial intelligence system to evolve cardinal feature-manifestations of brain`s (self-organizing)

V. R. Raju
{"title":"A study of brains complex organs-organisms with artificial intelligence system to evolve cardinal feature-manifestations of brain`s (self-organizing)","authors":"V. R. Raju","doi":"10.18231/j.ijn.2023.043","DOIUrl":null,"url":null,"abstract":": Embedding carnal (somatic or physical) restraints over the artificial intelligent system (i.e., artificially-intelligent system) in ample the similar way that the ‘human-brain’ must grow, progress plus function in the physically real, tangible and biological constrictions that lets system to advance feature-manifestations of the brains of multifaceted organs and organisms so as to solve brain issues. : Placing carnal restraints on AI-based model-system, i.e., artificially intelligent system. : spatially embedded recurrent neural nets (RNNs), 3D Euclidean space, where message of fundamental neural-cells are hampered by ‘sparse-connectome’ recurrent-neural-nets (RNN). : RNNs converge over anatomical, structural functional features universally originate within primates (cardinal, mandrill), and macaques’ cerebral/rational, brainy-cortices. Explicitly, they congregate/ (converge) over resolving implications via segmental (modular) tiny-world nets, in which functionally analogous-units spatially configure/construct themselves to use the dynamically effective varied-discerning code. Since features occur in union RNNs show how many mutual anatomical, functional-brain patterns (motifs) are deeply linked, can be ascribed to basic biologic optimization-processes. : RNNs merge biophysical limits in AI system plus aid as a bridge amid anatomical functional researchers to move ability neuroscience on.","PeriodicalId":415114,"journal":{"name":"IP Indian Journal of Neurosciences","volume":"435 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IP Indian Journal of Neurosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18231/j.ijn.2023.043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

: Embedding carnal (somatic or physical) restraints over the artificial intelligent system (i.e., artificially-intelligent system) in ample the similar way that the ‘human-brain’ must grow, progress plus function in the physically real, tangible and biological constrictions that lets system to advance feature-manifestations of the brains of multifaceted organs and organisms so as to solve brain issues. : Placing carnal restraints on AI-based model-system, i.e., artificially intelligent system. : spatially embedded recurrent neural nets (RNNs), 3D Euclidean space, where message of fundamental neural-cells are hampered by ‘sparse-connectome’ recurrent-neural-nets (RNN). : RNNs converge over anatomical, structural functional features universally originate within primates (cardinal, mandrill), and macaques’ cerebral/rational, brainy-cortices. Explicitly, they congregate/ (converge) over resolving implications via segmental (modular) tiny-world nets, in which functionally analogous-units spatially configure/construct themselves to use the dynamically effective varied-discerning code. Since features occur in union RNNs show how many mutual anatomical, functional-brain patterns (motifs) are deeply linked, can be ascribed to basic biologic optimization-processes. : RNNs merge biophysical limits in AI system plus aid as a bridge amid anatomical functional researchers to move ability neuroscience on.
利用人工智能系统对大脑复杂器官--有机体进行研究,以进化大脑的主要特征--表现形式(自组织)
:在人工智能系统(即人工智能系统)中植入肉体(躯体或物理)约束,其方式与 "人脑 "必须在物理上真实、有形和生物约束中成长、进步和发挥作用的方式完全相同,使系统能够推进多方面器官和生物体大脑的特征体现,从而解决大脑问题、:空间嵌入式递归神经网络(RNN),三维欧几里得空间,基本神经细胞的信息受到 "稀疏连接组 "递归神经网络(RNN)的阻碍。 RNN 汇集了灵长类动物(红心猕猴、山魈)和猕猴大脑皮层内普遍存在的解剖、结构功能特征。明确地说,它们通过分段(模块化)微小世界网(tiny-world nets)聚集/(汇聚)在解析意义上,其中功能类似的单元在空间上配置/构建自身,以使用动态有效的变化辨识代码。RNNs 融合了人工智能系统中的生物物理限制,并成为解剖学功能研究人员推动神经科学能力发展的桥梁。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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