Stream data processing has lately gained momentum with the arrival of new Big Data scenarios and applications dealing with continuously produced information flows. Unfortunately, traditional machine learning algorithms are not prepared to tackle the specific challenges imposed by data stream processing, such as the need for learning incrementally, limited memory and processing time requirements, and adaptation to non-stationary data, among others. To face these paradigms, Spiking Neural Networks have emerged as one of the most promising stream learning techniques, with variants such as Evolving Spiking Neural Networks capable of efficiently addressing many of these challenges. Interestingly, these networks resort to a particular population encoding scheme - Gaussian Receptive Fields - to transform the incoming stimuli into temporal spikes. The study presented in this manuscript sheds light on the predictive potential of this encoding scheme, focusing on how it can be applied as a computationally lightweight, model-agnostic preprocessing step for data stream learning. We provide informed intuition to unveil under which circumstances the aforementioned population encoding method yields effective prediction gains in data stream classification with respect to the case where no preprocessing is performed. Results obtained for a variety of stream learning models and both synthetic and real stream datasets are discussed to empirically buttress the capability of Gaussian Receptive Fields to boost the predictive performance of stream learning methods, spanning further research towards extrapolating our findings to other machine learning problems.

中文翻译：

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利用不断发展的Spiking神经网络的刺激编码方案进行流学习。

随着新的大数据场景和处理不断产生的信息流的应用程序的出现，流数据处理近来获得了动力。不幸的是，传统的机器学习算法还没有准备好应对数据流处理带来的特定挑战，例如，需要增量学习，有限的内存和处理时间要求以及对非平稳数据的适应性等等。面对这些范式，尖峰神经网络已经成为最有前途的流学习技术之一，其变体如演进尖峰神经网络能够有效解决许多挑战。有趣的是，这些网络诉诸于特定的种群编码方案-高斯接受场-将输入的刺激转换为时间尖峰。本手稿中的研究揭示了这种编码方案的预测潜力，重点在于如何将其用作计算轻量级，模型不可知的预处理步骤，以进行数据流学习。我们提供了直观的信息，以揭露在何种情况下，相对于未执行预处理的情况，上述总体编码方法在数据流分类中会产生有效的预测收益。讨论了从各种流学习模型以及合成流数据集和实际流数据集获得的结果，以经验方式支持高斯接受场增强流学习方法的预测性能的能力，涉及到进一步的研究，以将我们的发现推论到其他机器学习问题上。