ReviewNature communications2023
Toward a formal theory for computing machines made out of whatever physics offers.
Review in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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.
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Who cites it
27 citing papers in PubMed.
- Biomimetic ferroelectric-semiconductor transistor enables neuronal multisensory integration.Nature communications · 2026Article
- Embodying physical computing into soft robots.Nature communications · 2026Review
- A caveat regarding the unfolding argument: implications of plasticity.Neuroscience of consciousness · 2026Article
- Gradient descent in materia through homodyne gradient extraction.Nature communications · 2025Article
- Optical neuromorphic computing via temporal up-sampling and trainable encoding on a telecom device platform.Nanophotonics (Berlin, Germany) · 2025Article
- Seamless optical cloud computing across edge-metro network for generative AI.Nature communications · 2025Article
- Resistive Switching Random-Access Memory (RRAM): Applications and Requirements for Memory and Computing.Chemical reviews · 2025Review
- Lightweight error-tolerant edge detection using memristor-enabled stochastic computing.Nature communications · 2025Article
- Landauer's Principle: Past, Present and Future.Entropy (Basel, Switzerland) · 2025Article
- Self-organizing neuromorphic nanowire networks as stochastic dynamical systems.Nature communications · 2025Article
- HfZrO-based synaptic resistor circuit for a Super-Turing intelligent system.Science advances · 2025Article
- Femtojoule optical nonlinearity for deep learning with incoherent illumination.Science advances · 2025Article
- Harnessing spatiotemporal transformation in magnetic domains for nonvolatile physical reservoir computing.Science advances · 2025Article
- Human brain dynamics are shaped by rare long-range connections over and above cortical geometry.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines.Entropy (Basel, Switzerland) · 2024Article
- Potential and challenges of computing with molecular materials.Nature materials · 2024Review
- Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.Nature communications · 2024Article
- Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data.PLoS computational biology · 2024Review
- Machine learning without a processor: Emergent learning in a nonlinear analog network.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Application of physics encoded neural networks to improve predictability of properties of complex multi-scale systems.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Approaching limitations of digital computing technologies have spurred research in neuromorphic and other unconventional approaches to computing. Here we argue that if we want to engineer unconventional computing systems in a systematic way, we need guidance from a formal theory that is different from the classical symbolic-algorithmic Turing machine theory. We propose a general strategy for developing such a theory, and within that general view, a specific approach that we call fluent computing. In contrast to Turing, who modeled computing processes from a top-down perspective as symbolic reasoning, we adopt the scientific paradigm of physics and model physical computing systems bottom-up by formalizing what can ultimately be measured in a physical computing system. This leads to an understanding of computing as the structuring of processes, while classical models of computing systems describe the processing of structures.
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.