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Abstract Brain‐inspired (or neuromorphic) computing circumvents costly bottlenecks in conventional Von Neumann architectures by collocating memory and processing. This is accomplished through dynamic material architectures, strengthening or weakening internal conduction pathways similar to synaptic connections within the brain. A new class of neuromorphic materials approximates synaptic interfaces using lipid membranes assembled via the droplet interface bilayer (DIB) technique. These DIB membranes have been studied as novel memristors or memcapacitors owing to the soft, reconfigurable nature of both the lipid membrane geometry and the embedded ion‐conducting channels. In this work, a biomolecular approach to neuromorphic materials is expanded frommodel synapsesto acharge‐integrating model neuron. In these serial membrane networks, it is possible to create distributions of voltage‐sensitive gates capable of trapping ionic charge. This trapped charge creates transmembrane potential differences that drive changes in the system's net capacitance through electrowetting, providing a synaptic weight that changes in response to the history and timing of input signals. This fundamental change from interfacial memory (dimensions of the membrane) to internal memory (charge trapped within the droplets) provides a functional plasticity capable of multiple weights, longer‐term retention roughly an order of magnitude greater than memory stored in the membranes alone, and programming‐erasure.
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Heterosynaptic plasticity in biomembrane memristors controlled by pH
Abstract In biology, heterosynaptic plasticity maintains homeostasis in synaptic inputs during associative learning and memory, and initiates long-term changes in synaptic strengths that nonspecifically modulate different synapse types. In bioinspired neuromorphic circuits, heterosynaptic plasticity may be used to extend the functionality of two-terminal, biomimetic memristors. In this article, we explore how changes in the pH of droplet interface bilayer aqueous solutions modulate the memristive responses of a lipid bilayer membrane in the pH range 4.97–7.40. Surprisingly, we did not find conclusive evidence for pH-dependent shifts in the voltage thresholds ( V* ) needed for alamethicin ion channel formation in the membrane. However, we did observe a clear modulation in the dynamics of pore formation with pH in time-dependent, pulsed voltage experiments. Moreover, at the same voltage, lowering the pH resulted in higher steady-state currents because of increased numbers of conductive peptide ion channels in the membrane. This was due to increased partitioning of alamethicin monomers into the membrane at pH 4.97, which is below the pKa (~5.3–5.7) of carboxylate groups on the glutamate residues of the peptide, making the monomers more hydrophobic. Neutralization of the negative charges on these residues, under acidic conditions, increased the concentration of peptide monomers in the membrane, shifting the equilibrium concentrations of peptide aggregate assemblies in the membrane to favor greater numbers of larger, increasingly more conductive pores. It also increased the relaxation time constants for pore formation and decay, and enhanced short-term facilitation and depression of the switching characteristics of the device. Modulating these thresholds globally and independently of alamethicin concentration and applied voltage will enable the assembly of neuromorphic computational circuitry with enhanced functionality. Impact statement We describe how to use pH as a modulatory “interneuron” that changes the voltage-dependent memristance of alamethicin ion channels in lipid bilayers by changing the structure and dynamical properties of the bilayer. Having the ability to independently control the threshold levels for pore conduction from voltage or ion channel concentration enables additional levels of programmability in a neuromorphic system. In this article, we note that barriers to conduction from membrane-bound ion channels can be lowered by reducing solution pH, resulting in higher currents, and enhanced short-term learning behavior in the form of paired-pulse facilitation. Tuning threshold values with environmental variables, such as pH, provide additional training and learning algorithms that can be used to elicit complex functionality within spiking neural networks. Graphical abstract
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- Award ID(s):
- 2219289
- PAR ID:
- 10402226
- Date Published:
- Journal Name:
- MRS Bulletin
- Volume:
- 48
- Issue:
- 1
- ISSN:
- 0883-7694
- Page Range / eLocation ID:
- 13 to 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Synaptic plasticity refers to activity-dependent synaptic strengthening or weakening between neurons. It is usually associated with homosynaptic plasticity, which refers to a synaptic junction controlled by interactions between specific neurons. Heterosynaptic plasticity, on the other hand, lacks this specificity. It involves much larger populations of synapses and neurons and can be associated with changes in synaptic strength due to nonlocal alterations in the ambient electrochemical environment. This paper presents specific examples demonstrating how variations in the ambient electrochemical environment of lipid membranes can impact the nonlinear dynamical behaviors of memristive and memcapacitive systems in droplet interface bilayers (DIBs). Examples include the use of pH as a modulatory factor that alters the voltage-dependent memristive behavior of alamethicin ion channels in DIB lipid bilayers, and the discovery of long-term potentiation (LTP) in a lipid bilayer-only system after application of electrical stimulation protocols.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1557/s43577-022-00344-z
