论文标题
在标准尖峰神经网络中，柔软结合的回忆突触的强大学习规则具有竞争力
A Robust Learning Rule for Soft-Bounded Memristive Synapses Competitive with Supervised Learning in Standard Spiking Neural Networks
论文作者
论文摘要
回忆设备是一类电路元素，显示出巨大的希望，作为脑启发计算的未来构建障碍。理论神经科学中一种有影响力的观点将大脑视为一种功能计算设备：给定输入信号，大脑应用功能以生成新的内部状态和电动机输出。因此，能够近似函数是一个基本的公理，以便将来的大脑研究并得出更有效的计算机。在这项工作中，我们将一种新颖的监督学习算法（基于控制尼伯族掺杂的钛酸锶的复活突触）来学习非平凡的多维功能。通过将我们的方法实施到尖峰神经网络模拟器Nengo中，我们表明我们能够至少与使用理想，线性突触时获得的性能匹配，并且 - 这样做 - 可以将这种回忆设备作为计算基板作为计算底物，以朝着更有效的脑力，脑力启发的计算方向发展。
Memristive devices are a class of circuit elements that shows great promise as future building block for brain-inspired computing. One influential view in theoretical neuroscience sees the brain as a function-computing device: given input signals, the brain applies a function in order to generate new internal states and motor outputs. Therefore, being able to approximate functions is a fundamental axiom to build upon for future brain research and to derive more efficient computational machines. In this work we apply a novel supervised learning algorithm - based on controlling niobium-doped strontium titanate memristive synapses - to learning non-trivial multidimensional functions. By implementing our method into the spiking neural network simulator Nengo, we show that we are able to at least match the performance obtained when using ideal, linear synapses and - in doing so - that this kind of memristive device can be harnessed as computational substrate to move towards more efficient, brain-inspired computing.
