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Microsaccades for asynchronous feature extraction with spiking networks

机译：微刺用于利用尖刺网络进行异步特征提取

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While extracting spatial features from images has been studied for decades, extracting spatio-temporal features from event streams is still a young field of research. A particularity of event streams is that the same network architecture can be used for recognition of static objects or motions. However, it is not clear what features provide a good abstraction and in what scenario. In this paper, we evaluate the quality of the features of a spiking HMAX architecture by computing classification performance before and after each layer. We demonstrate the abstraction capability of classical edge features, as were found in the V1 area of the visual cortex, combined with fixational eye movements. Specifically, our performance on N-Caltech101 dataset outperforms previously reported F1 score on Caltech101, with a similar architecture but without a STDP learning layer. However, we show that the same edge features do not manage to abstract motions observed with a static DVS from the DvsGesture dataset. Additionally, we show that liquid state machines are a promising computational model for the classification of DVS data with temporal dynamics. This paper is a step forward towards understanding and reproducing biological vision.
机译：尽管从图像中提取空间特征已经进行了数十年的研究，但从事件流中提取时空特征仍然是一个年轻的研究领域。事件流的特殊之处在于，可以将相同的网络体系结构用于静态对象或运动的识别。但是，尚不清楚哪些功能可以提供良好的抽象以及在什么情况下提供。在本文中，我们通过计算每层之前和之后的分类性能来评估尖峰HMAX架构的功能的质量。我们展示了在视觉皮层的V1区域中发现的经典边缘特征与固定眼动相结合的抽象能力。具体而言，我们在N-Caltech101数据集上的性能优于以前报道的在Caltech101上的F1分数，具有类似的体系结构，但没有STDP学习层。但是，我们显示出相同的边缘特征无法从DvsGesture数据集中提取使用静态DVS观察到的运动。此外，我们证明了液态状态机是一种用于将DVS数据随时间动态分类的有前途的计算模型。本文是朝着理解和再现生物视觉迈出的一步。

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