ArticleRoyal Society open science2023
Learning in ensembles of proteinoid microspheres.
Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Fast and Slow Signal Propagation in Abiotic Polypeptide Assemblies.Chembiochem : a European journal of chemical biology · 2026Article
- Biohybrid Computing with Proteinoids and Algae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Modulation of Proteinoid Electrical Spiking Activity with Magnetic Nanoparticles.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- On Emergence of Spontaneous Oscillations in Kombucha and Proteinoids.BioNanoScience · 2025Review
- Optical Recognition of the English Alphabet Using Proteinoids.ACS omega · 2024Article
- Proteinoids-Polyaniline Interaction with Stimulated Neurons on Living and Plastic Surfaces.ACS omega · 2024Article
- The Effects of Omeprazole on the Neuron-like Spiking of the Electrical Potential of Proteinoid Microspheres.Molecules (Basel, Switzerland) · 2024Article
- On Effect of Chloroform on Electrical Activity of Proteinoids.Biomimetics (Basel, Switzerland) · 2024Article
- Memfractance of Proteinoids.ACS omega · 2024Article
- Learning in ensembles of proteinoid microspheres.Royal Society open science · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proteinoids are thermal proteins which form microspheres in water in the presence of salt. Ensembles of proteinoid microspheres exhibit passive nonlinear electrical properties and active neuron-like spiking of electrical potential. We propose that various neuromorphic computing architectures can be prototyped from the proteinoid microspheres. A key feature of a neuromorphic system is a learning. Through the use of optical and resistance measurements, we study mechanisms of learning in ensembles of proteinoid microspheres. We analyse 16 types of proteinoids study and their intrinsic morphology and electrical properties. We demonstrate that proteinoids can learn, memorize and habituate, making them a promising candidate for novel computing.
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Registered trials
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