Neuromorphic learning, working memory, and metaplasticity in nanowire networks.

Nanowire networks (NWNs) mimic the brain's neurosynaptic connectivity and emergent dynamics. Consequently, NWNs may also emulate the synaptic processes that enable higher-order cognitive functions such as learning and memory. A quintessential cognitive task used to measure human working memory is the n-back task. In this study, task variations inspired by the n-back task are implemented in a NWN device, and external feedback is applied to emulate brain-like supervised and reinforcement learning. NWNs are found to retain information in working memory to at least n = 7 steps back, remarkably similar to the originally proposed "seven plus or minus two" rule for human subjects. Simulations elucidate how synapse-like NWN junction plasticity depends on previous synaptic modifications, analogous to "synaptic metaplasticity" in the brain, and how memory is consolidated via strengthening and pruning of synaptic conductance pathways.

Information dynamics in neuromorphic nanowire networks.

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

Avalanches and edge-of-chaos learning in neuromorphic nanowire networks.

The brain's efficient information processing is enabled by the interplay between its neuro-synaptic elements and complex network structure. This work reports on the neuromorphic dynamics of nanowire networks (NWNs), a unique brain-inspired system with synapse-like memristive junctions embedded within a recurrent neural network-like structure. Simulation and experiment elucidate how collective memristive switching gives rise to long-range transport pathways, drastically altering the network's global state via a discontinuous phase transition. The spatio-temporal properties of switching dynamics are found to be consistent with avalanches displaying power-law size and life-time distributions, with exponents obeying the crackling noise relationship, thus satisfying criteria for criticality, as observed in cortical neuronal cultures. Furthermore, NWNs adaptively respond to time varying stimuli, exhibiting diverse dynamics tunable from order to chaos. Dynamical states at the edge-of-chaos are found to optimise information processing for increasingly complex learning tasks. Overall, these results reveal a rich repertoire of emergent, collective neural-like dynamics in NWNs, thus demonstrating the potential for a neuromorphic advantage in information processing.

Sleep-Dependent Memory Consolidation in a Neuromorphic Nanowire Network.

A neuromorphic network composed of silver nanowires coated with TiO2 is found to show certain parallels with neural networks in nature such as biological brains. Owing to the memristive properties emerging at nanowire-to-nanowire contacts, where the Ag/TiO2/Ag interface exists, the network can store information in the form of connectivity between nanowires in the network as electrically measured as an increase in conductance. The observed memory arises from an interplay between the topological constraints imposed by a complex network structure and the plasticity of its constituting memristive Ag/TiO2/Ag junctions. Regarding the long-term decay of the connectivity in the network, we further investigate the controllability of the established connectivity. Inspired by the regulated activity cycles of the human brain during sleep, a learning-sleep-recovery cycle was mimicked by applying voltage pulses, with controlling pulse heights and duty ratios, to the nanowire network. Interestingly, even when the conductance was lost during sleep, the network could quickly recover previous states of conductance in the recovery process after sleep. Comparison between results of experiments and theoretical simulations revealed that such a quick recovery of conductance can be realized by sparse voltage pulse application during sleep; in other words, sleep-dependent memory consolidation occurs and can be controlled. The present results provide clues to new learning designs in neuromorphic networks for achieving longer memory retention for future neuromorphic technology.

Topological Properties of Neuromorphic Nanowire Networks.

Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and functional properties of human neural networks. Here, we apply graph theory to a model of a novel neuromorphic system constructed from self-assembled nanowires, whose structure and function may mimic that of human neural networks. Simulations of neuromorphic nanowire networks allow us to directly examine their topology at the individual nanowire-node scale. This type of investigation is currently extremely difficult experimentally. We then apply network cartographic approaches to compare neuromorphic nanowire networks with: random networks (including an untrained artificial neural network); grid-like networks and the structural network of C. elegans. Our results demonstrate that neuromorphic nanowire networks exhibit a small-world architecture similar to the biological system of C. elegans, and significantly different from random and grid-like networks. Furthermore, neuromorphic nanowire networks appear more segregated and modular than random, grid-like and simple biological networks and more clustered than artificial neural networks. Given the inextricable link between structure and function in neural networks, these results may have important implications for mimicking cognitive functions in neuromorphic nanowire networks.

Emergent dynamics of neuromorphic nanowire networks.

Neuromorphic networks are formed by random self-assembly of silver nanowires. Silver nanowires are coated with a polymer layer after synthesis in which junctions between two nanowires act as resistive switches, often compared with neurosynapses. We analyze the role of single junction switching in the dynamical properties of the neuromorphic network. Network transitions to a high-conductance state under the application of a voltage bias higher than a threshold value. The stability and permanence of this state is studied by shifting the voltage bias in order to activate or deactivate the network. A model of the electrical network with atomic switches reproduces the relation between individual nanowire junctions switching events with current pathway formation or destruction. This relation is further manifested in changes in 1/f power-law scaling of the spectral distribution of current. The current fluctuations involved in this scaling shift are considered to arise from an essential equilibrium between formation, stochastic-mediated breakdown of individual nanowire-nanowire junctions and the onset of different current pathways that optimize power dissipation. This emergent dynamics shown by polymer-coated Ag nanowire networks places this system in the class of optimal transport networks, from which new fundamental parallels with neural dynamics and natural computing problem-solving can be drawn.

Importance of point defect reactions for the atomic-scale roughness of III-V nanowire sidewalls.

The surface morphology of III-V semiconductor nanowires (NWs) protected by an arsenic cap and subsequently evaporated in ultrahigh vacuum is investigated with scanning tunneling microscopy and scanning transmission electron microscopy. We show that the changes of the surface morphology as a function of the NW composition and the nature of the seed particles are intimately related to the formation and reaction of surface point defects. Langmuir evaporation close to the congruent evaporation temperature causes the formation of vacancies which nucleate and form vacancy islands on {110} sidewalls of self-catalyzed InAs NWs. However, for annealing temperatures much smaller than the congruent temperature, a new phenomenon occurs: group III vacancies form and are filled by excess As atoms, leading to surface AsGa antisites. The resulting Ga adatoms nucleate with excess As atoms at the NW edges, producing monoatomic-step islands on the {110} sidewalls of GaAs NWs. Finally, when gold atoms diffuse from the seed particle onto the {110} sidewalls during evaporation of the protective As cap, Langmuir evaporation does not take place, leaving the sidewalls of InAsSb NWs atomically flat.

Shallow Heavily Doped n++ Germanium by Organo-Antimony Monolayer Doping.

Functionalization of Ge surfaces with the aim of incorporating specific dopant atoms to form high-quality junctions is of particular importance for the development of solid-state devices. In this study, we report the shallow doping of Ge wafers with a monolayer doping strategy that is based on the controlled grafting of Sb precursors and the subsequent diffusion of Sb into the wafer upon annealing. We also highlight the key role of citric acid in passivating the surface before its reaction with the Sb precursors and the benefit of a protective SiO2 overlayer that enables an efficient incorporation of Sb dopants with a concentration higher than 1020 cm-3. Microscopic four-point probe measurements and photoconductivity experiments show the full electrical activation of the Sb dopants, giving rise to the formation of an n++ Sb-doped layer and an enhanced local field-effect passivation at the surface of the Ge wafer.

Nonstoichiometric Low-Temperature Grown GaAs Nanowires.

The structural and electronic properties of nonstoichiometric low-temperature grown GaAs nanowire shells have been investigated with scanning tunneling microscopy and spectroscopy, pump-probe reflectivity, and cathodoluminescence measurements. The growth of nonstoichiometric GaAs shells is achieved through the formation of As antisite defects, and to a lower extent, after annealing, As precipitates. Because of the high density of atomic steps on the nanowire sidewalls, the Fermi level is pinned midgap, causing the ionization of the subsurface antisites and the formation of depleted regions around the As precipitates. Controlling their incorporation offers a way to obtain unique electronic and optical properties that depart from the ones found in conventional GaAs nanowires.