Current efforts to achieve neuromorphic computation are focused on highly organized architectures, such as integrated circuits and regular arrays of memristors, which lack the complex interconnectivity of the brain and so are unable to exhibit brain-like dynamics. New architectures are required, both to emulate the complexity of the brain and to achieve critical dynamics and consequent maximal computational performance. We show here that electrical signals from self-organized networks of nanoparticles exhibit brain-like spatiotemporal correlations and criticality when fabricated at a percolating phase transition. Specifically, the sizes and durations of avalanches of switching events are power law distributed, and the power law exponents satisfy rigorous criteria for criticality. These signals are therefore qualitatively and quantitatively similar to those measured in the cortex. Our self-organized networks provide a low-cost platform for computational approaches that rely on spatiotemporal correlations, such as reservoir computing, and are an important step toward creating neuromorphic device architectures.

Self-assembled networks of nanoparticles and nanowires have recently emerged as promising systems for brain-like computation. Here we focus on percolating networks of nanoparticles which exhibit brain-like dynamics. We use a combination of experiments and simulations to show that the brain-like network dynamics emerge from atomic-scale switching dynamics inside tunnel gaps that are distributed throughout the network. The atomic-scale dynamics emulate leaky integrate and fire (LIF) mechanisms in biological neurons leading to the generation of critical avalanches of signals.These avalanches are quantitatively the same as those observed in cortical tissue and are signatures of the correlations that are required for computation. We show that the avalanches are associated with dynamical restructuring of the networks which selftune to balanced states consistent with self-organised criticality. Our simulations allow visualisation of the network states and detailed mechanisms of signal propagation.

Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing.

It is becoming clear that tokamak anomalous transport is dominated by radially extended non-local modes which originate from strong toroidal coupling of rational surfaces in non-uniform plasmas. To aid in understanding the internal transport barrier (ITB) formed in reversed magnetic shear experiments, in addition to the well known shear flow effect, the article points out an important non-local effect and/or finite size effect which comes from the complex behaviour of the mode over a finite radial region around the minimum q (safety factor) surface. The non-local mode, which is characterized by its radial extent and the degree of tilting in the poloidal direction (Δr, θ0), changes its structure depending on the sign of the magnetic shear, and as a result such modes are weakly excited across the qmin surface. This leads to a discontinuity or gap which disconnects the phase relation in the global wave structure across the qmin surface. Once such a discontinuity (or gap) is formed, transport suppression occurs and therefore a transport barrier can be expected near the qmin surface. The existence of this discontinuity is confirmed through use of a toroidal particle simulation. It is also shown that whether such a discontinuity is efficiently established depends on the presence of the radial electric field and the related plasma shear flow.

We report a detailed study of neuromorphic switching behaviour in inherently complex percolating networks of self-assembled metal nanoparticles. We show that variation of the strength and duration of the electric field applied to this network of synapse-like atomic switches allows us to control the switching dynamics. Switching is observed for voltages above a well-defined threshold, with higher voltages leading to increased switching rates. We demonstrate two behavioral archetypes and show how the switching dynamics change as a function of duration and amplitude of the voltage stimulus. We show that the state of each synapse can influence the activity of the other synapses, leading to complex switching dynamics. We further demonstrate the influence of the morphology of the network on the measured device properties, and the constraints imposed by the overall network conductance. The correlated switching dynamics, device stability over long periods, and the simplicity of the device fabrication provide an attractive pathway to practical implementation of on-chip neuromorphic computing. arXiv:1812.09865v1 [cond-mat.dis-nn]

Josephson current between two d-wave superconductors is calculated by using a lattice model. Here we consider two types of junctions, i.e., the parallel junction and the mirror-type junction. The maximum Josephson current (J c ) shows a wide variety of temperature (T ) dependence depending on the misorientation angles and the types of junctions. When the misorientation angles are not zero, the Josephson current shows the low-temperature anomaly because of a zero energy state (ZES) at the interfaces. In the case of mirror-type junctions, J c has a non monotonic temperature dependence. These results are consistent with the previous results based on the quasiclassical theory.[Y. Tanaka and S. Kashiwaya: Phys. Rev. B 56 (1997) 892.] On the other hand, we find that the ZES disappears in several junctions because of the Freidel oscillations of the wave function, which is peculiar to the lattice model. In such junctions, the temperature dependence of J c is close to the Ambegaokar-Baratoff relation.

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Electric-field-induced nuclear resonance (NER: nuclear electric resonance) involving quantum Hall states (QHSs) was studied at various filling factors by exploiting changes in nuclear spins polarized at quantum Hall breakdown. Distinct from the magnetic dipole interaction in nuclear magnetic resonance, the interaction of the electric-field gradient with the electric quadrupole moment plays the dominant role in the NER mechanism. The magnitude of the NER signal strongly depends on whether electronic states are localized or extended. This indicates that NER is sensitive to the screening capability of the electric field associated with QHSs.