论文标题
人工神经网络中的随机共振神经元
Stochastic resonance neurons in artificial neural networks
论文作者
论文摘要
人工神经网络的许多现代应用随之而来的是大量层，使传统数字实施越来越复杂。光学神经网络在高带宽下提供并行处理，但面临噪声积累的挑战。我们在这里提出了一种新型的神经网络，使用随机共振作为体系结构的固有部分，并证明了以给定性能准确性大量减少所需神经元数量的可能性。我们还表明，这种神经网络对噪声的影响更强大。
Many modern applications of the artificial neural networks ensue large number of layers making traditional digital implementations increasingly complex. Optical neural networks offer parallel processing at high bandwidth, but have the challenge of noise accumulation. We propose here a new type of neural networks using stochastic resonances as an inherent part of the architecture and demonstrate a possibility of significant reduction of the required number of neurons for a given performance accuracy. We also show that such a neural network is more robust against the impact of noise.
