ArticleScientific reports2021
Information dynamics in neuromorphic nanowire networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- On the molecular basis of enduring memory in neurons, and cell fate in fibroblasts.Nucleic acids research · 2026Review
- Training-free AI for earth observation change detection using physics aware neuromorphic networks.Scientific reports · 2025Article
- Next-generation graph computing with electric current-based and quantum-inspired approaches.Nature communications · 2025Review
- Self-organizing neuromorphic nanowire networks as stochastic dynamical systems.Nature communications · 2025Article
- The connectivity degree controls the difficulty in reservoir design of random boolean networks.Frontiers in computational neuroscience · 2024Article
- Online dynamical learning and sequence memory with neuromorphic nanowire networks.Nature communications · 2023Article
- Tomography of memory engrams in self-organizing nanowire connectomes.Nature communications · 2023Article
- Neuromorphic learning, working memory, and metaplasticity in nanowire networks.Science advances · 2023Article
- Dynamical stochastic simulation of complex electrical behavior in neuromorphic networks of metallic nanojunctions.Scientific reports · 2022Article
- In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks.Nature materials · 2022Article
- Entropy, Economics, and Criticality.Entropy (Basel, Switzerland) · 2022Article
- Avalanches and edge-of-chaos learning in neuromorphic nanowire networks.Nature communications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
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.