摘要翻译:
本文提出了计算网络(CNs)框架。CNs被用来推广神经和群体体系结构。人工神经网络、蚁群优化、粒子群优化和现实的生物模型被用作CNS的实例。将这些体系结构描述为CNs允许对它们进行比较。它们的差异和相似性允许识别属性,使神经和群体架构能够执行复杂的计算并展示复杂的认知能力。在此背景下,中枢神经系统最相关的特征是存在多个动力学和功能尺度。讨论了多个动态和功能尺度与适应、认知(大脑和群体)和计算的关系。
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英文标题:
《Computing Networks: A General Framework to Contrast Neural and Swarm
Cognitions》
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作者:
Carlos Gershenson
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最新提交年份:
2010
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Neural and Evolutionary Computing 神经与进化计算
分类描述:Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络,连接主义,遗传算法,人工生命,自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Physics 物理学
二级分类:Adaptation and Self-Organizing Systems 自适应和自组织系统
分类描述:Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应,自组织系统,统计物理,波动系统,随机过程,相互作用粒子系统,机器学习
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英文摘要:
This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures. Artificial neural networks, ant colony optimization, particle swarm optimization, and realistic biological models are used as examples of instantiations of CNs. The description of these architectures as CNs allows their comparison. Their differences and similarities allow the identification of properties that enable neural and swarm architectures to perform complex computations and exhibit complex cognitive abilities. In this context, the most relevant characteristics of CNs are the existence multiple dynamical and functional scales. The relationship between multiple dynamical and functional scales with adaptation, cognition (of brains and swarms) and computation is discussed.
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PDF链接:
https://arxiv.org/pdf/1001.5244