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Updated: Oct 5, 2025

Semi-automated Optical Heartbeat Analysis of Small Hearts
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Biological computation: hearts and flytraps.

Kay L Kirkpatrick1

  • 1Departments of Mathematics and Physics, University of Illinois at Urbana-Champaign, Illinois, USA. kkirpat@illinois.edu.

Journal of Biological Physics
|January 28, 2022
PubMed
Summary
This summary is machine-generated.

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This study introduces biological computation, a new model suited for biological systems like hearts and flytraps. It reframes computation to bridge the gap between human and machine learning.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Artificial Intelligence

Background:

  • Historically, computation was human-driven, evolving to digital computers and artificial neural networks.
  • Current models of the brain as a computer, including artificial neural networks, have limitations in explaining biological complexity.
  • Classical computability theory may not fully capture the nuances of biological systems.

Purpose of the Study:

  • To define and examine a novel computational framework termed biological computation.
  • To explore biological computation using biological systems as case studies, specifically hearts and Venus flytraps.
  • To advance neuroscience by offering a new perspective on computation in living organisms.

Main Methods:

  • Defining biological computation as a natural adaptation of mechanistic physical computation.
Keywords:
Biological computationBiological information processingComputationComputational theory of mindNeural computationNeurocardiology

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  • Analyzing the computational capabilities of the heart and Venus flytrap.
  • Comparing the computational power of these biological systems to existing benchmarks.

    • Main Results:

    • The heart exhibits significant computation occurring outside its neurons, comparable to a slug's computing power.
    • The Venus flytrap demonstrates computational capacity akin to a lobster ganglion.
    • Biological computation offers a more nuanced view than classical computability theory for biological complexity.

    Conclusions:

    • Biological computation provides a framework better adapted to biological systems than traditional computational models.
    • This new perspective highlights limitations in viewing nervous systems solely through the lens of digital computers.
    • Reframing computation is crucial for resolving the disconnect between human and machine learning.