The lattice dynamics of the metallic tin β and γ polymorphs has been studied by a combination of diffuse scattering, inelastic x-ray scattering and density functional perturbation theory. The non-symmorphic space group of the β-tin structure results in unusual asymmetry of thermal diffuse scattering. Strong resemblance of the diffuse scattering intensity distribution in β and γ-tin were observed, reflecting the structural relationship between the two phases and revealing the qualitative similarity of the underlying electronic potential. The strong influence of the electron subsystem on inter-ionic interactions creates anomalies in the phonon dispersion relations. All observed features are described in great detail by the density functional perturbation theory for both β- and γ-tin at arbitrary momentum transfers. The combined approach delivers thus a complete picture of the lattice dynamics in harmonic description.
Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving.
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