Brain-like critical dynamics and long-range temporal correlations in percolating networks of silver nanoparticles and functionality preservation after integration of insulating matrix

Abstract

Random networks of nanoparticle-based memristive switches enable pathways for emulating highly complex and self-organized synaptic connectivity together with their emergent functional behavior known from biological neuronal networks. They therefore embody...

Gas-phase synthesis of nanoparticles: current application challenges and instrumentation development responses

“…The resistive switching behaviour was attributed to the stochastic formation and destruction of nanojunctions (filament formation) between neighbouring clusters leading to conducting pathways of various lengths under the effect of applied voltage. This behaviour, recently investigated in depth via Ag nanoparticle networks, 57 strongly resembles neuron firing signals, exhibiting brain-like long-range temporal correlations that are similarly measured in the cortex. 58 Consecutively, Vahl et al 59 demonstrated the resistive switching property of individual Ag-Au and Ag-Pt alloy nanoparticles embedded in a thin SiO 2 matrix by using the non-invasive conductive atomic force microscopy (C-AFM) technique.…”

Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity

“…[22][23][24][25] By definition, the fabrication of an active element is straightforward, involving simple synthesis routes and depositing on pre-made electrodes. Several such hierarchical systems, such as networks of nanowires with core-shell structures, 26,27 atomic switches, 22,24 and single-walled carbon nanotubes, 28,29 as well as percolating tunnelling gaps in metallic nanoparticles [30][31][32] have been shown to imitate biologically plausible properties like self-organisation, 24,30 adaptability, 27,33 leaky integrate and fire, 34 long-range temporal correlations, and small-worldness. 31 It is perceived that hierarchical features give more versatility in terms of how the G behaviour can be manipulated over wide ranges.…”

Reservoir computing using networks of memristors: effects of topology and heterogeneity

“…2 Much effort has been invested in developing neuromorphic integrated circuits based on complementary metal–oxide semiconductor technologies 3,4 but additionally several nanoscale systems and components show promise, especially spintronic oscillators, 5 and self-organised networks of memristive devices. 6,7 Self organised systems, which include carbon nanotube, 8,9 nanowire 10–14 and nanoparticle 15–18 networks, are appealing because they have the potential to naturally integrate large numbers of memristive devices into brain-like structures that are difficult (or practically impossible) to attain using top-down processes, with low fabrication costs.…”