Listen to the Waves: Using a Neuronal Model of the Human Auditory System
  to Predict Ocean Waves
Artur Matysiak Volker Roeber Henrik Kalisch Reinhard König Patrick J. C. May
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.