基于忆阻神经网络的联想记忆实验研究
Experimental demonstration of associative memory with memristive neural networks
摘要：
当有人提到一个熟悉的人的名字时，我们会立即回忆起她的脸，可能还有其他许多特征。这是因为我们拥有所谓的联想记忆，即能够将不同的记忆与同一事实或事件联系起来。联想记忆是一种基本的、涵盖人类（不仅仅是人类）的能力，我们大脑中的神经元网络必须很容易地执行它。接下来的问题是，电子神经网络（一种类似于人脑的电子机制）是否可以被构建来执行这类功能。虽然神经网络领域已经发展了很多年，但是一个关键元素，即相邻神经元之间的突触，一直缺乏令人满意的电子表示。其原因在于，能够再现突触行为的无源电路元件需要记住其过去的动态历史，存储连续的状态集合，并且根据突触前和突触后神经元活动是“可塑的”。在这里，我们展示了所有这些都可以通过记忆电阻器（简称忆阻器）来实现。特别是，通过使用简单和廉价的现成的组件，我们已经建立了一个忆阻器仿真器，实现了所有需要的突触特性。最重要的是，我们已经通过实验证明了在一个简单的神经网络中形成联想记忆，该网络由两个忆阻器-仿真器突触连接的三个电子神经元组成。这一实验演示为使用记忆设备理解神经过程开辟了新的可能性，这是用电子神经网络再现复杂学习、自适应和自发行为的重要一步。
英文摘要：
When someone mentions the name of a known person we immediately recall her face and possibly many other traits. This is because we possess the so-called associative memory, that is the ability to correlate different memories to the same fact or event. Associative memory is such a fundamental and encompassing human ability (and not just human) that the network of neurons in our brain must perform it quite easily. The question is then whether electronic neural networks (electronic schemes that act somewhat similarly to human brains) can be built to perform this type of function. Although the field of neural networks has developed for many years, a key element, namely the synapses between adjacent neurons, has been lacking a satisfactory electronic representation. The reason for this is that a passive circuit element able to reproduce the synapse behaviour needs to remember its past dynamical history, store a continuous set of states, and be "plastic" according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive off-the-shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behaviour with electronic neural networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
