论文标题
部分可观测时空混沌系统的无模型预测
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks
论文作者
论文摘要
尖峰神经网络（SNN）作为一种受脑启发的方法，由于其潜力产生超高能量高效的硬件而引起了人们的关注。基于峰值依赖性可塑性（STDP）的竞争学习是训练无监督的SNN的流行方法。但是，通过此方法训练的先前的无监督的SNN仅限于一个可学习层的浅网络，与多层SNN相比，无法获得令人满意的结果。在本文中，我们通过：1）我们提出了一个尖峰（SP-Inception）模块，灵感来自人工神经网络（ANN）文献中的启发。该模块通过基于STDP的竞争学习进行培训，并优于学习能力，学习效率和鲁棒性的基线模块。 2）我们提出了一个合并的振动激活（PRA）层，以使SP-Inpection模块可堆叠。 3）我们堆叠了多个SP-Inception模块以构建多层SNN。我们的算法在手写数字分类任务上优于基线算法，并在现有无监督的SNN中达到MNIST数据集的最新结果。
Spiking Neural Network (SNN), as a brain-inspired approach, is attracting attention due to its potential to produce ultra-high-energy-efficient hardware. Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a popular method to train an unsupervised SNN. However, previous unsupervised SNNs trained through this method are limited to a shallow network with only one learnable layer and cannot achieve satisfactory results when compared with multi-layer SNNs. In this paper, we eased this limitation by: 1)We proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in the Artificial Neural Network (ANN) literature. This module is trained through STDP-based competitive learning and outperforms the baseline modules on learning capability, learning efficiency, and robustness. 2)We proposed a Pooling-Reshape-Activate (PRA) layer to make the Sp-Inception module stackable. 3)We stacked multiple Sp-Inception modules to construct multi-layer SNNs. Our algorithm outperforms the baseline algorithms on the hand-written digit classification task, and reaches state-of-the-art results on the MNIST dataset among the existing unsupervised SNNs.
