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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., Comment: 23 pages, 6 figures

This paper introduces ARCH-Elites, a MAP-Elites implementation that can reconfigure large-scale urban layouts at real-world locations via a pre-trained surrogate model instead of costly simulations. In a series of experiments, we generate novel urban designs for two real-world locations in Boston, Massachusetts. Combining the exploration of a possibility space with real-time performance evaluation creates a powerful new paradigm for architectural generative design that can extract and articulate design intelligence., Comment: Published at Genetic and Evolutionary Computation Conference Companion, 2021, 2 pages, 1 figure

We discuss the strain dependence of the acoustic properties of amorphous metals in both normal and superconducting states, in the temperature range 0.1 mK $ \le T \le 1 $K. A crossover is found when the strain energy is of the order of the effective interaction energy between tunneling systems at the corresponding temperature. Our results provide clear evidence for the interaction between tunneling systems, whose energy is in quantitative agreement with theoretical expectations, and reveal that without the knowledge of the corresponding strain dependences, the measured temperature dependences below $\sim 50 $mK of the acoustic properties of disordered solids are rather meaningless., Comment: 13 pages, 3 figures

Compacted platinum powders exhibit a sharp onset of diamagnetic screening at $T \simeq 1.9$ mK in zero magnetic field in all samples investigated. This sharp onset is interpreted in terms of the intragranular transition into the superconducting state. At lower temperatures, the magnetic ac susceptibility strongly depends on the ac field amplitude and reflects the small intergranular critical current density $j_{c}$. This critical current density shows a strong dependence on the packing fraction f of the granular samples. Surprisingly, $j_{c}$ increases significantly with decreasing f ($j_{c}(B=0, T=0) \simeq 0.07$ A/cm$^{2}$ for f = 0.67 and $j_{c}(B=0, T=0) \simeq 0.8$ A/cm$^{2}$ for f = 0.50). The temperature dependence of $j_{c}$ shows strong positive curvature over a wide temperature range for both samples. The phase diagrams of inter- and intragranular superconductivity for different samples indicate that the granular structure might play the key role for an understanding of the origin of superconductivity in the platinum compacts., Comment: 11 pages including 9 figures. To appear in Phys. Rev. B in Nov. 00