论文标题
时空分类的异质性复发神经网络
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification
论文作者
论文摘要
尖峰神经网络通常被吹捧为第三波人工智能的脑启发学习模型。尽管最近接受有监督的反向传播训练的SNN表现出与深网络相当的分类精度，但基于学习的SNN的性能仍然要低得多。本文提出了一个异质的复发性尖峰神经网络（HRSNN），并在RGB（KTH，UCF11，UCF11，UCF101）和基于事件的数据集（DVS128 GESTURE）的RGB（KTH，UCF11，UCF11，UCF11，UCF11，UCF11，UCF11，UCF11，UCF11，UCF11，UCF11）上进行了无监督学习的学习。 HRSNN的主要新颖性是，HRSNN中的复发层由具有不同射击/放松动态的异质神经元组成，并且它们通过不同的学习动力学的异质性峰值依赖性塑性（STDP）进行了培训。我们表明，建筑和学习方法中异质性的这种新颖组合优于当前同质尖峰神经网络。我们进一步表明，HRSNN可以实现与最新的经过培训的受过监督的SNN相似的性能，但是计算较少（神经元和稀疏连接）和较少的培训数据。
Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This paper presents a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets (DVS128 Gesture). The key novelty of the HRSNN is that the recurrent layer in HRSNN consists of heterogeneous neurons with varying firing/relaxation dynamics, and they are trained via heterogeneous spike-time-dependent-plasticity (STDP) with varying learning dynamics for each synapse. We show that this novel combination of heterogeneity in architecture and learning method outperforms current homogeneous spiking neural networks. We further show that HRSNN can achieve similar performance to state-of-the-art backpropagation trained supervised SNN, but with less computation (fewer neurons and sparse connection) and less training data.
