Design of a Biomolecular Neuristor Circuit for Bioinspired Control

Ahmed S. Mohamed, Ashlee S. Liao, Yongjie Jessica Zhang, Victoria A. Webster-Wood, Joseph S. Najem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


The nervous system serves as an inspiration for control and processing systems but can be difficult to replicate or directly implement using biological neurons in bioinspired and biohybrid systems. An alternative path is to use synthetic biomolecular neuristors that are inspired by biological neurons and can closely mimic their spiking behavior. A neuristor is built using two dynamical lipid-bilayer-based devices that exhibit volatile memory and negative differential resistance arising from the dynamics of voltage-gated ion channels and the ion gradients across the lipid membranes. The firing pattern and frequency can be easily tuned by engineering the composition of each neuristor. To investigate the viability of using biomolecular neuristors to design neural control circuits, a neuristor computational model was implemented and compared to that of the Izhikevich neuron model. Both models were assessed for individual cells and then arranged to form mutually inhibitory circuits, as central pattern generators commonly found in motor control circuits. Both models can replicate the alternating firing behavior, but further parameter tuning is needed for the neuristor model to better match the firing frequency of the biological neuron model.

Original languageEnglish (US)
Title of host publicationBiomimetic and Biohybrid Systems - 11th International Conference, Living Machines 2022, Proceedings
EditorsAlexander Hunt, Vasiliki Vouloutsi, Kenneth Moses, Roger Quinn, Anna Mura, Tony Prescott, Paul F. Verschure
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031204692
StatePublished - 2022
Event11th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2022 - Virtual, Online
Duration: Jul 19 2022Jul 22 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13548 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2022
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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