Assembly and characterization of biomolecular memristors consisting of ion channel-doped lipid membranes

Joseph S. Najem, Graham J. Taylor, Nick Armendarez, Ryan J. Weiss, Md Sakib Hasan, Garrett S. Rose, Catherine D. Schuman, Alex Belianinov, Stephen A. Sarles, C. Patrick Collier

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The ability to recreate synaptic functionalities in synthetic circuit elements is essential for neuromorphic computing systems that seek to emulate the cognitive powers of the brain with comparable efficiency and density. To date, silicon-based three-terminal transistors and two-terminal memristors have been widely used in neuromorphic circuits, in large part due to their ability to co-locate information processing and memory. Yet these devices cannot achieve the interconnectivity and complexity of the brain because they are power-hungry, fail to mimic key synaptic functionalities, and suffer from high noise and high switching voltages. To overcome these limitations, we have developed and characterized a biomolecular memristor that mimics the composition, structure, and switching characteristics of biological synapses. Here, we describe the process of assembling and characterizing biomolecular memristors consisting of a 5 nm-thick lipid bilayer formed between lipid-functionalized water droplets in oil and doped with voltage-activated alamethicin peptides. While similar assembly protocols have been used to investigate biophysical properties of droplet-supported lipid membranes and membrane-bound ion channels, this article focuses on key modifications of the droplet interface bilayer method essential for achieving consistent memristor performance. Specifically, we describe the liposome preparation process and the incorporation of alamethicin peptides in lipid bilayer membranes, and the appropriate concentrations of each constituent as well as their impact on the overall response of the memristors. We also detail the characterization process of biomolecular memristors, including measurement and analysis of memristive current-voltage relationships obtained via cyclic voltammetry, as well as short-term plasticity and learning in response to step-wise voltage pulse trains.

Original languageEnglish (US)
Article numbere58998
JournalJournal of Visualized Experiments
Issue number145
StatePublished - Mar 2019

All Science Journal Classification (ASJC) codes

  • General Neuroscience
  • General Chemical Engineering
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology


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