Hopfield J J, Brody C D
Department of Molecular Biology, Princeton University, Princeton, NJ 08544-1014, USA.
Proc Natl Acad Sci U S A. 2000 Dec 5;97(25):13919-24. doi: 10.1073/pnas.250483697.
Recognition of complex temporal sequences is a general sensory problem that requires integration of information over time. We describe a very simple "organism" that performs this task, exemplified here by recognition of spoken monosyllables. The network's computation can be understood through the application of simple but generally unexploited principles describing neural activity. The organism is a network of very simple neurons and synapses; the experiments are simulations. The network's recognition capabilities are robust to variations across speakers, simple masking noises, and large variations in system parameters. The network principles underlying recognition of short temporal sequences are applied here to speech, but similar ideas can be applied to aspects of vision, touch, and olfaction. In this article, we describe only properties of the system that could be measured if it were a real biological organism. We delay publication of the principles behind the network's operation as an intellectual challenge: the essential principles of operation can be deduced based on the experimental results presented here alone. An interactive web site (http://neuron.princeton.edu/ approximately moment) is available to allow readers to design and carry out their own experiments on the organism.
对复杂时间序列的识别是一个普遍的感官问题,需要整合一段时间内的信息。我们描述了一种执行此任务的非常简单的“生物体”,这里以对单音节语音的识别为例。通过应用描述神经活动的简单但通常未被利用的原理,可以理解该网络的计算过程。该生物体是一个由非常简单的神经元和突触组成的网络;实验是模拟。该网络的识别能力对于不同说话者、简单的掩蔽噪声以及系统参数的大变化具有鲁棒性。识别短时间序列所依据的网络原理在此应用于语音,但类似的想法也可应用于视觉、触觉和嗅觉方面。在本文中,我们仅描述如果该系统是一个真实生物体会可测量的属性。我们推迟公布该网络运行背后的原理,作为一个智力挑战:仅基于此处呈现的实验结果就可以推断出基本的运行原理。有一个交互式网站(http://neuron.princeton.edu/ approximately moment)可供读者对该生物体进行自己的设计和实验。