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.

翻译：神经形态计算系统模仿生物神经系统的电生理行为,使用混合模式模拟或数字VLSI电路。这些系统在执行认知任务时显示出较高的精确度和功率。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作为时间函数运行在钉钉列上。神经形态编码器将感官数据转换成钉钉列。本文采用了低功率神经形态编码器,用于图像处理。图像像素和间歇间距之间的数学模型也得到了开发。在像素和间歇间距之间的指数关系得到了实现。最后,数学公式通过电路模拟得到验证。