ArticleNetwork neuroscience (Cambridge, Mass.)2020
Long-range temporal correlations in scale-free neuromorphic networks.
Article in Network neuroscience (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Inertia mechanisms in networks and knowledge diffusion in team inertia networks.Scientific reports · 2025Article
- Neuronal oscillatory imbalances in GNAO1-related disorders associated with disease severity.Epilepsia · 2025Observational
- Emergence of Polymer-Networked Nanoparticle Structures as Primitive Neuromorphic Computing States.The journal of physical chemistry. A · 2025Article
- Neuromorphic Computing Primitives Using Polymer-Networked Nanoparticles.The journal of physical chemistry. C, Nanomaterials and interfaces · 2024Article
- Stable Millivolt Range Resistive Switching in Percolating Molybdenum Nanoparticle Networks.ACS applied materials & interfaces · 2024Article
- Tomography of memory engrams in self-organizing nanowire connectomes.Nature communications · 2023Article
- The fractal brain: scale-invariance in structure and dynamics.Cerebral cortex (New York, N.Y. : 1991) · 2023Review
- Brain-like critical dynamics and long-range temporal correlations in percolating networks of silver nanoparticles and functionality preservation after integration of insulating matrix.Nanoscale advances · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.