Unsupervised visual feature learning with spike-timing-dependent plasticity: How far are we from traditional feature learning approaches?
Unsupervised visual feature learning with spike-timing-dependent plasticity: How far are we from traditional feature learning approaches?
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DOI：
10.1016/j.patcog.2019.04.016
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发表时间：
2019-09-01
影响因子：
8
通讯作者：
Boulet, Pierre
中科院分区：
文献类型：
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作者：
Falez, Pierre;Tirilly, Pierre;Boulet, Pierre
配备延迟编码和尖峰定时相关可塑性规则的尖峰神经网络（SNN）提供了解决标准计算机视觉方法的数据和能量瓶颈的替代方案：它们可以在没有监督的情况下学习视觉特征，并且可以通过超低功耗硬件架构实现。然而，它们在图像分类中的性能从未在最近的图像数据集上进行过评估。在本文中，我们比较SNN的自动编码器在三个视觉识别数据集，并扩展SNN的使用彩色图像。结果的分析有助于我们识别SNN的一些瓶颈：对中心/偏离中心编码的限制，特别是对于彩色图像，以及当前抑制机制的无效性。这些问题应该得到解决，以建立有效的SNN图像识别。(C)2019爱思唯尔有限公司版权所有。
Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures. However, their performance in image classification has never been evaluated on recent image datasets. In this paper, we compare SNNs to auto-encoders on three visual recognition datasets, and extend the use of SNNs to color images. The analysis of the results helps us identify some bottlenecks of SNNs: the limits of on-center/off-center coding, especially for color images, and the ineffectiveness of current inhibition mechanisms. These issues should be addressed to build effective SNNs for image recognition. (C) 2019 Elsevier Ltd. All rights reserved.