Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, highly parallel operation of brains. However, to use these systems in applications, we need "neuromorphic algorithms" that can run on them. Here we develop a spiking neural network model for neuromorphic hardware that uses spike timing-dependent plasticity and lateral inhibition to perform unsupervised clustering. With this model, time-invariant, rate-coded datasets can be mapped into a feature space with a specified resolution, i.e., number of clusters, using exclusively neuromorphic hardware. We developed and tested implementations on the SpiNNaker neuromorphic system and on GPUs using the GeNN framework. We show that our neuromorphic clustering algorithm achieves results comparable to those of conventional clustering algorithms such as self-organizing maps, neural gas or k-means clustering. We then combine it with a previously reported supervised neuromorphic classifier network to demonstrate its practical use as a neuromorphic preprocessing module.

中文翻译：

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一种无监督的神经形态聚类算法。

大脑使用常规计算机上执行相同功能所需的一部分功率来执行复杂的任务。新的神经形态硬件系统现在正变得广泛可用，其目的是模仿更节能，高度并行的大脑操作。但是，要在应用程序中使用这些系统，我们需要可以在它们上运行的“神经形态算法”。在这里，我们为神经形态硬件开发了一个尖峰神经网络模型，该模型使用依赖于峰值时序的可塑性和横向抑制来执行无监督聚类。使用此模型，可以使用专用神经形态硬件将时不变率编码的数据集映射到具有指定分辨率（即簇数）的特征空间。我们使用GeNN框架在SpiNNaker神经形态系统和GPU上开发和测试了实现。我们证明了我们的神经形态聚类算法所获得的结果可与传统聚类算法（如自组织图，神经气体或k均值聚类）相媲美。然后，我们将其与以前报告的监督神经形态分类器网络结合起来，以证明其实际用作神经形态预处理模块。