论文标题
图像识别的时间神经形态编码器的间尖峰间隔的设计和数学建模
Design and Mathematical Modelling of Inter Spike Interval of Temporal Neuromorphic Encoder for Image Recognition
论文作者
论文摘要
神经形态计算系统使用混合模拟或数字VLSI电路模仿生物神经系统的电生理行为。这些系统在执行认知任务时表现出卓越的准确性和功率效率。神经形态计算系统中使用的神经网络结构是类似于生物神经系统的神经网络（SNN）。 SNN随着时间的变化在尖峰火车上运行。神经形态编码器将感觉数据转换为尖峰火车。在本文中，实施了用于图像处理的低功耗神经形态编码器。还制定了图像像素与尖峰间隔之间的数学模型。其中获得了像素和尖峰间隔之间的指数关系。最后，通过电路模拟验证了数学方程。
Neuromorphic computing systems emulate the electrophysiological behavior of the biological nervous system using mixed-mode analog or digital VLSI circuits. These systems show superior accuracy and power efficiency in carrying out cognitive tasks. The neural network architecture used in neuromorphic computing systems is spiking neural networks (SNNs) analogous to the biological nervous system. SNN operates on spike trains as a function of time. A neuromorphic encoder converts sensory data into spike trains. In this paper, a low-power neuromorphic encoder for image processing is implemented. A mathematical model between pixels of an image and the inter-spike intervals is also formulated. Wherein an exponential relationship between pixels and inter-spike intervals is obtained. Finally, the mathematical equation is validated with circuit simulation.
