Artificial neural networks (ANNs) have evolved from the 1940s primitive models of brain function to become tools for artificial intelligence. They comprise many units, artificial neurons, interlinked through weighted connections. ANNs are trained to perform tasks through learning rules that modify the connection weights. With these rules being in the focus of research, ANNs have become a branch of machine learning developing independently from neuroscience. Although likely required for the development of truly intelligent machines, the integration of neuroscience into ANNs has remained a neglected proposition. Here, we demonstrate that designing an ANN along biological principles results in drastically improved task performance. As a challenging real-world problem, we choose real-time ocean-wave prediction which is essential for various maritime operations. Motivated by the similarity of ocean waves measured at a single location to sound waves arriving at the eardrum, we redesign an echo state network to resemble the brain's auditory system. This yields a powerful predictive tool which is computationally lean, robust with respect to network parameters, and works efficiently across a wide range of sea states. Our results demonstrate the advantages of integrating neuroscience with machine learning and offer a tool for use in the production of green energy from ocean waves.

翻译：人工神经网络（ANNs）源于20世纪40年代大脑功能的原始模型，现已发展为人工智能工具。其由众多通过加权连接相互关联的人工神经元单元构成。通过调整连接权重的学习规则进行训练。随着学习规则成为研究焦点，人工神经网络已发展为独立于神经科学的机器学习分支。尽管神经科学与人工神经网络的融合可能是开发真正智能机器的必要条件，但这一方向仍被忽视。本文证明，基于生物学原理设计的人工神经网络能显著提升任务性能。我们选取对海洋作业至关重要的实时海浪预测作为现实挑战，受单点测量的海浪与鼓膜接收声波相似性的启发，将回声状态网络重新设计为类脑听觉系统。由此获得的预测工具计算精简、网络参数鲁棒性强，且能高效适应多种海况。该结果证明了神经科学与机器学习融合的优势，并为海洋波浪能绿色能源生产提供了实用工具。