Nature communication|利用超顺磁隧道结群体编码的基函数进行神经形态计算 ...

在神经生物学领域，种群编码理论证明了大量的神经元可以用来进行信息的容错处理。纳米电子学使得我们可以利用小尺寸的、充满噪声的、并不完美的器件实现可靠的计算。这种想法要求群体编码的神经元根据他们对输入信号的响应形成一组基函数，为计算的物理实现提供基础。这样的群体编码计算原理也可以使用CMOS电路来实现，但是需要较高的能耗要求。来自法国的Alice Mizrahi等人提出了可以利用磁性隧道结实现群体编码神经形态计算的想法，并满足电路低功耗的要求，从实验上证明了九个隧道结构成的网络可以实现一组基函数，他们基于内部相互连接的隧道结实现了混合CMOS-Spintronics 电路，并用此电路实现了信息的非线性低功耗转换。

In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for computing. Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements. Here, we show that nanoscale magnetic tunnel junctions can instead be assembled to meet these requirements. We demonstrate experimentally that a population of nine junctions can implement a basis set of functions, providing the data to achieve, for example, the generation of cursive letters. We design hybrid magnetic-CMOS systems based on interlinked populations of junctions and show that they can learn to realize non-linear variabilityresilient transformations with a low imprint area and low power.