NEURONS WITH GRADED RESPONSE HAVE COLLECTIVE COMPUTATIONAL PROPERTIES LIKE THOSE OF 2-STATE NEURONS
NEURONS WITH GRADED RESPONSE HAVE COLLECTIVE COMPUTATIONAL PROPERTIES LIKE THOSE OF 2-STATE NEURONS
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DOI：
10.1073/pnas.81.10.3088
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发表时间：
1984-01-01
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
研究具有分级响应（或S形输入输出关系）的大型神经元网络模型。这个确定性系统具有集体性质，与早期基于McCulloch-Pitts神经元的随机模型非常接近。原始模型的内容寻址记忆和其他涌现的集体属性也出现在分级反应模型中。这种集体性质在生物系统中使用的想法因更接近生物神经元的这种性质的持续存在而更加可信。所描述的那种集体模拟电路肯定会起作用。两个模型的集体态具有简单的对应关系。原来的模型将继续是有用的模拟，因为它的连接到分级响应系统的建立。方程，包括在分级反应系统的动作电位的影响也被开发。
A model for a large network of neurons with a graded response (or sigmoid input-output relation) is studied. This deterministic system has collective properties in very close correspondence with the earlier stochastic model based on McCulloch-Pitts neurons. The content-addressable memory and other emergent collective properties of the original model also are present in the graded response model. The idea that such collective properties are used in biological systems is given added credence by the continued presence of such properties for more nearly biological neurons. Collective analog electrical circuits of the kind described will certainly function. The collective states of the 2 models have a simple correspondence. The original model will continue to be useful for simulations, because its connection to graded response systems is established. Equations that include the effect of action potentials in the graded response system are also developed.