Neural networks have been employed for a wide range of processing applications like image processing, motor control, object detection and many others. Living neural networks offer advantages of lower power consumption, faster processing, and biological realism. Optogenetics offers high spatial and temporal control over biological neurons and presents potential in training live neural networks. This work proposes a simulated living neural network trained indirectly by backpropagating STDP based algorithms using precision activation by optogenetics achieving accuracy comparable to traditional neural network training algorithms.

翻译：神经网络已被广泛应用于图像处理、运动控制、目标检测等多种处理任务。生物神经网络具有功耗更低、处理速度更快以及生物真实性的优势。光遗传学能够对生物神经元实现高时空精度的控制，为训练活体神经网络提供了可能。本研究提出一种模拟活体神经网络，通过基于脉冲时序依赖可塑性（STDP）的反向传播算法进行间接训练，并利用光遗传学的精准激活机制，达到了与传统神经网络训练算法相当的精度。