Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Beyond input-output computings: error-driven emergence with parallel non-distributed slime mold computer.

Masashi Aono1, Yukio-Pegio Gunji

  • 1Department of Information Media Sciences, Graduate School of Science and Technology, Kobe University, Nada, Kobe 657-8501, Japan. 993d802n@y01.kobe-u.ac.jp

Bio Systems
|October 18, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interplay Between Vertical and Horizontal Schemes of Computation: From Bayesian Inference to Quantum Logic via Gluing Boolean Algebras.

Entropy (Basel, Switzerland)·2026
Same author

Adaptive inference through Bayesian and inverse Bayesian inference with symmetry bias in nonstationary environments.

Bio Systems·2026
Same author

Enhancing Neuromorphic Robustness via Recurrence Resonance: The Role of Shared Weak Attractors in Quantum Logic Networks.

Biomimetics (Basel, Switzerland)·2026
Same author

Quantum logic automata generate class IV-like patterns and 1/f noise.

Bio Systems·2024
Same author

Feeling of hand deformation as a monkey's hand: an experiment on a visual body with discomfort and its algebraic analysis.

Frontiers in neuroscience·2023
Same author

Connecting the free energy principle with quantum cognition.

Frontiers in neurorobotics·2022
Same journal

Ruliological Resilience: Pattern Restoration and Robustness in Wolfram Patterns. A Basis for Regeneration, Not Just in Cone Shells?

Bio Systems·2026
Same journal

The quantum-to-classical transducer: A thermodynamic and quantum mechanical framework for the emergence of bioenergetics.

Bio Systems·2026
Same journal

Forward-backward gene expression binarization for boolean state inference over a known regulatory network.

Bio Systems·2026
Same journal

Partial-label metric ceilings for evaluating gene regulatory networks inferred from single-cell foundation models.

Bio Systems·2026
Same journal

The impedance mismatch theory: A non-equilibrium thermodynamic framework for a shared energetic stress pathway in neurodegeneration.

Bio Systems·2026
Same journal

Immune signal-status misclassification: A theoretical framework for biological status assignment and failed status resolution.

Bio Systems·2026
See all related articles

This study introduces a biological computing approach using slime mold to solve complex problems. This novel method leverages emergent properties for efficient computation, offering a new paradigm in computer science.

Area of Science:

  • Complex Systems
  • Biological Computing
  • Computational Science

Background:

  • Emergence from errors is crucial for novel computing paradigms.
  • Complex systems exhibit emergent properties that can be harnessed for computation.
  • Biological systems offer unique computational capabilities.

Purpose of the Study:

  • To propose an experimental plan for biological computing using slime mold.
  • To elicit emergent properties of complex systems through slime mold computing.
  • To explore slime mold's capability in solving NP-complete problems.

Main Methods:

  • Utilizing an individual plasmodium of Physarum polycephalum as a biological computer.
  • Modifying the Elementary Cellular Automaton to address global synchronization in parallel computing.

Related Experiment Videos

  • Implementing a slime mold computer to solve NP-complete problems.

    • Main Results:

    • The slime mold computer can solve NP-complete problems.
    • Demonstrates a novel approach to biological computing.
    • Highlights the dynamic distributivity in local computing logic.

    Conclusions:

    • Slime mold computing offers a unique approach to complex problem-solving.
    • Parallel non-distributed computing in slime molds cannot be reduced to serial computations.
    • Absence of a super-system in computing can lead to emergent capabilities.