Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with different levels of biological plausibility and different computational features and complexities. Consequently, there is a need to define the right level of abstraction from biology in order to get the best performance in accurate, efficient and fast inference in neuromorphic hardware. In this context, we explore the impact of synaptic and membrane leakages in spiking neurons. We confront three neural models with different computational complexities using feedforward and recurrent topologies for event-based visual and auditory pattern recognition. Our results show that, in terms of accuracy, leakages are important when there are both temporal information in the data and explicit recurrence in the network. In addition, leakages do not necessarily increase the sparsity of spikes flowing in the network. We also investigate the impact of heterogeneity in the time constant of leakages, and the results show a slight improvement in accuracy when using data with a rich temporal structure. These results advance our understanding of the computational role of the neural leakages and network recurrences, and provide valuable insights for the design of compact and energy-efficient neuromorphic hardware for embedded systems.
翻译:与神经形态硬件和事件感应器相结合的Spik神经网络以及神经形态硬件和事件感应器对边缘低纬度和低功率推断越来越感兴趣,然而,文献中提出了多种神经突触模型,其生物可观性和计算特征和复杂性各不相同,因此,需要确定生物学的正确提取水平,以便取得准确、高效和快速的神经形态硬件的最佳性能。在此情况下,我们探索神经神经元体和膜渗漏的影响。我们面对三种具有不同计算复杂性的神经神经元模型,使用基于事件的视觉和听觉模式识别的进食和经常性表层模型。我们的结果显示,在准确性方面,如果数据中既包含时间信息,又明确重现网络中,渗漏是十分重要的。此外,渗漏并不一定会增加网络中振动系统的紧张性。我们还调查渗漏时间常态和膜渗漏中杂质渗漏的影响。我们面对三种不同计算复杂性的神经神经元模型,使用基于基于事件的进向前向和常态的神经形态结构的精确度,在利用数据结构的精确度和精确度分析时,这些精确度结构的精确度上提供了对能量结构的精确度的精确度。