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Related Experiment Video

Updated: Mar 26, 2026

Generation of a Human iPSC-Based Blood-Brain Barrier Chip

Generation of a Human iPSC-Based Blood-Brain Barrier Chip

Published on: March 2, 2020

Towards a Physarum learning chip.

James G H Whiting¹, Jeff Jones¹, Larry Bull¹

¹International Centre in Unconventional Computing, University of the West of England, Bristol, UK.

Scientific Reports

|February 4, 2016

Summary

This summary is machine-generated.

Slime mold networks exhibit programmable plasticity, altering tube conductivity via electrical stimulation. This slime mold computation offers insights into learning in non-neural biological networks.

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Area of Science:

Biocomputing
Network Plasticity
Non-neural Information Processing

Background:

The slime mold Physarum polycephalum forms optimal networks to navigate attractants and repellents.
This organism exhibits computational abilities by mapping environmental data into its network structure.

Purpose of the Study:

To investigate physical methods for programming slime mold networks.
To explore the role of protoplasmic tube conductivity in network plasticity and computation.

Main Methods:

Encouraging slime mold growth across an electrode grid.
Applying specific AC voltage stimuli (low and high frequency) to induce pathway-dependent plasticity.
Corroborating findings with a particle model.

Main Results:

Low-frequency AC stimulation (10 Hz, 0.5 V) increased connectivity between stimulated electrodes.
High-frequency AC stimulation (1000 Hz, 2.5 V) decreased connectivity between stimulated electrodes.
Demonstrated pathway-dependent plasticity in slime mold network conductivity.

Conclusions:

Slime mold networks display learning and weighted connections through electrical stimulation.
This plasticity mechanism offers a potential pathway for slime mold-based computation.
Findings may illuminate information processing in non-neural biological systems.