集体神经网络的化学实现与热力学
Chemical implementation and thermodynamics of collective neural networks.
作者信息
Hjelmfelt A, Ross J
机构信息
Max-Planck-Institut für Biophysikalische Chemie, Göttingen, Federal Republic of Germany.
出版信息
Proc Natl Acad Sci U S A. 1992 Jan 1;89(1):388-91. doi: 10.1073/pnas.89.1.388.
Abstract
The chemical implementation of a neuron and connections among neurons described in prior work is used to construct collective neural networks. With stated approximations, these chemical networks are reduced to networks of the Hopfield type. Chemical networks approaching a stationary or equilibrium state provide a Liapunov function with the same extremal properties as Hopfield's energy function. Numerical comparisons of chemical and Hopfield networks with small numbers (2-16) of neurons show agreement on the results of given computations.
摘要
先前工作中所描述的神经元的化学实现方式以及神经元之间的连接，被用于构建集体神经网络。在给定的近似条件下，这些化学网络被简化为霍普菲尔德类型的网络。趋近于稳定或平衡状态的化学网络提供了一个具有与霍普菲尔德能量函数相同极值特性的李雅普诺夫函数。对具有少量（2 - 16个）神经元的化学网络和霍普菲尔德网络进行的数值比较表明，在给定计算结果上两者是一致的。
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