NEURAL NETWORKS AND PHYSICAL SYSTEMS WITH EMERGENT COLLECTIVE COMPUTATIONAL ABILITIES
NEURAL NETWORKS AND PHYSICAL SYSTEMS WITH EMERGENT COLLECTIVE COMPUTATIONAL ABILITIES
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DOI：
10.1073/pnas.79.8.2554
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发表时间：
1982-01-01
期刊：
影响因子：
--
通讯作者：
HOPFIELD, JJ
中科院分区：
文献类型：
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作者：
HOPFIELD, JJ
用于生物有机体或构建计算机的计算特性可以作为具有大量简单等价组件(或神经元)的系统的集体特性出现。内容可寻址存储器的物理意义由系统状态的适当相空间流来描述。给出了这样一个系统的模型，它基于神经生物学的各个方面，但很容易适应集成电路。这个模型的集体属性产生了一个内容可寻址的内存，它可以从任何足够大小的子部分正确地产生整个内存。系统状态的时间演化算法是基于异步并行处理的。其他新出现的集体属性包括一些概括、熟悉性识别、分类、纠错和时序保持的能力。集体属性仅对建模的细节或单个设备的故障不太敏感。
Computational properties of use to biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.