Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning.
Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning.
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DOI：
10.1155/2020/8851351
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发表时间：
2020
影响因子：
3.1
通讯作者：
Luo S
中科院分区：
文献类型：
--
作者：
Li X;Yi H;Luo S
电生理学研究表明，哺乳动物初级视皮层对视觉刺激的方向具有选择性。受此机制启发，我们提出了一种用于图像分类的分层尖峰神经网络(SNN)。灰度输入图像通过由方向选择神经元组成的前馈网络馈送，然后通过基于脉冲的监督TEPOTRON学习规则将其投影到下游分类器神经元层。基于视觉皮层的方位选择机制和tempotron学习规则，该网络能够有效地对被广泛研究的手写数字MNIST数据库的图像进行分类，仅基于2,000个训练样本(传统训练集为60000)，分类准确率达到96%。与其他分类方法相比，该模型不仅保证了图像分类的生物真实性和准确性，而且大大减少了所需的训练样本。考虑到最常用的深度学习神经网络在图像识别中需要大数据样本和高功耗，这种基于视觉皮质逐层分层图像处理机制的脑启发计算神经网络模型可能为脉冲神经网络在智能计算领域的广泛应用提供基础。
Electrophysiological studies have shown that mammalian primary visual cortex are selective for the orientations of visual stimuli. Inspired by this mechanism, we propose a hierarchical spiking neural network (SNN) for image classification. Grayscale input images are fed through a feed-forward network consisting of orientation-selective neurons, which then projected to a layer of downstream classifier neurons through the spiking-based supervised tempotron learning rule. Based on the orientation-selective mechanism of the visual cortex and tempotron learning rule, the network can effectively classify images of the extensively studied MNIST database of handwritten digits, which achieves 96% classification accuracy based on only 2000 training samples (traditional training set is 60000). Compared with other classification methods, our model not only guarantees the biological plausibility and the accuracy of image classification but also significantly reduces the needed training samples. Considering the fact that the most commonly used deep learning neural networks need big data samples and high power consumption in image recognition, this brain-inspired computational neural network model based on the layer-by-layer hierarchical image processing mechanism of the visual cortex may provide a basis for the wide application of spiking neural networks in the field of intelligent computing.
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影响因子：
--
作者：
Izhikevich, EM
通讯作者：
Izhikevich, EM
影响因子：
5.5
作者：
HUBEL, DH;WIESEL, TN
通讯作者：
WIESEL, TN
影响因子：
--
作者：
Izhikevich, EM
通讯作者：
Izhikevich, EM
影响因子：
3.7
作者：
Li, Xiumin;Luo, Shengyuan;Xue, Fangzheng
通讯作者：
Xue, Fangzheng
影响因子：
5.6
作者：
Liao, YH;Vemuri, VR
通讯作者：
Vemuri, VR