For computer scientists, creation of neuromorphic systems — those inspired by and modeled after the way neurons in the human brain are structured — has been a longstanding goal.对计算机科学家而言，创建仿照神经形态体系仍然是一个长年目标，该点子源自且仿效人类大脑中神经元的包含方式。Now, in a significant step toward the development of neuromorphic technologies, a group of researchers IBMs research laboratory in Zurich have announced that they have built a working, artificial version of a neuron.如今，作为关于神经形态技术的发展尤的最重要一步，坐落于苏黎世的IBM实验室中，一组研究人员宣告他们早已发明者了一个正在运营的人造神经元。

The invention, described in a paper published in the journal Nature Nanotechnology, consists of a small square of germanium antimony telluride held between two electrodes. Germanium antimony telluride, a common ingredient in optical disks, is what is known as phase-change material. This means it can change its phase from an amorphous insulator to a crystalline conductor when hit with a strong enough electric pulse — thus acting like both, a resister and capacitor, and mimicking, to a certain extent, the behavior of biological neurons lipid bilayer membrane.此项发明者在《大自然纳米技术》杂志上公开发表，此发明者由两个电极之间一小块锗锑碲包含。锗锑碲是制作光盘的少见材料，也就是所谓的热力学材料。这就意味著当遇上充足强劲的电子脉冲时，其需要从有为形态绝缘体改变为晶体态导体，因此它的工作原理既看起来电阻器又看起来电容器，从或许上来说，它仿效了生物神经脂质双分子层的特性。In the published demonstration, the team applied a series of electrical pulses to the artificial neurons, which resulted in the progressive crystallization of the phase-change material, ultimately causing the neuron to fire. In neuroscience, this function is known as the integrate-and-fire property of biological neurons, IBM said in a statement released last Wednesday. This is the foundation for event-based computation and, in principle, is similar to how our brain triggers a response when we touch something hot. IBM在上周三公开发表的一份声明中称之为：在公布的展示中，该团队在人造神经元上产生了一系列电子脉冲，使热力学材料大大结晶，最后造成神经元点燃。

在神经科学领域，这一功能被称作生物神经元的构建--点燃属性，它是基于事件的计算出来基础。从原理上说道，与人们认识某些热东西后大脑的反应一样。This is not the only similarity between IBMs neurons and their organic counterparts. The artificial structures also exhibit stochasticity, or the ability to produce random, unpredictable results. Biological neurons are stochastic due to fluctuations within the cell — such as changes in ionic conductance and thermal background — while these artificial neurons are stochastic because the amorphous state of germanium antimony telluride always changes slightly after each reset.这不是IBM神经元与其有机变体的唯一相似性。

人造结构也展现出出有了随机特性，或者需要产生随机的、不能预测的结果。由于细胞内部的波动，生物神经元是随机的，诸如离子导电的变化、冷背底的变化，而人造神经元也展现出出有了随机特性，因为锗锑碲的有为形态在每次废黜后都有严重的变化。So why is this stochasticity — which makes the output of a system inherently unpredictable — desirable in an artificial neuron? As the researchers explain, stochasticity lets the neurons accomplish tasks that they would not be able to do if their output were perfectly predictable — something that may eventually lead to the creation of efficient cognitive computers that mimic the parallel processing architecture of the human brain.那么为什么人造神经元具备随机特性，使得系统输入本身具备不可预测性？正如研究人员所说明的，随机性使得神经元需要已完成一些任务，这些任务在输入几乎可以预测的情况下是无法已完成的，这有可能最后不会促成高效理解计算机的发明者，借以仿效与人类大脑平行的处置架构。

Populations of stochastic phase-change neurons, combined with other nanoscale computational elements such as artificial synapses, could be a key enabler for the creation of a new generation of extremely dense neuromorphic computing systems, co-author Tomas Tuma said in the statement.年出版者托马斯·图马在声明中回应：融合诸如人造神经神经元等其他纳米计算出来元素，随机热力学神经元群体沦为发明者新一代高密度神经形态计算出来体系的最重要推动者。

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