It is widely recognized that biological synapses are responsible for the high efficiency and enormous parallelism of the brain due to their ability to learn and process information in highly adaptive ways. This coordinated functionality emerges from multiple, highly complex molecular mechanisms that drive both short-term and long-term synaptic plasticity1,2,3,4,5. Neuromorphic computing systems aim to emulate synaptic functionalities at levels approaching the density, complexity, and energy efficiency of the brain, which are needed for the next generation of brain-like computers6,7,8. However, reproducing synaptic features using traditional electronic circuit elements is virtually impossible9, instead requiring the design and fabrication of new hardware elements that can adapt to incoming signals and remember information histories9. These types of synapse-inspired hardware are known as mem-elements9,10,11 (short for memory elements), which, according to Di Ventra et al.9,11, are passive, two-terminal devices whose resistance, capacitance, or inductance can be reconfigured in response to external stimuli, and which can remember prior states11. To achieve energy consumption levels approaching those in the brain, these elements should employ similar materials and mechanisms for synaptic plasticity12.
To date, two-terminal memristors13,14,15 have predominantly been built using complementary metal-oxide-semiconductor (CMOS) technology, characterized by high-switching voltages and high noise. This technology does not scale well due to high power consumption and low density. To address these limitations, multiple organic and polymeric memristors have been recently built. However, these devices exhibit significantly slower switching dynamics due to time-consuming ion diffusion through a conductive polymer matrix16,17. As a result, the mechanisms by which both CMOS-based and organic memristive devices emulate synapse-inspired functionalities are highly phenomenological, encompassing only a few synaptic functionalities such as Spike Timing Dependent Plasticity (STDP)18, while overlooking other key features that also play essential roles in making the brain a powerful and efficient computer, such as pre-synaptic, short-term plasticity19.
Recently, we introduced a new class of memristive devices12 featuring voltage-activated peptides incorporated in biomimetic lipid membranes that mimics the biomolecular composition, membrane structure, and ion channel triggered switching mechanisms of biological synapses20. Here, we describe how to assemble and electrically interrogate these two-terminal devices, with specific focus on how to evaluate short-term plasticity for implementation in online learning applications12. Device assembly is based on the droplet interface bilayer (DIB)21 method, which has been used extensively in recent years to study the biophysics of model membranes21 and membrane-bound ion channels22,23,24, and as building blocks for the development of stimuli-responsive materials25,26. We describe the membrane assembly and interrogation process in detail for those interested in neuromorphic applications but have limited experience in biomaterials or membrane biology. The protocol also includes a full description of the characterization procedure, which is as important as the assembly process, given the dynamic and reconfigurable electrical properties of the device27. The procedure and representative results described here are foundations for a new class of low-cost, low-power, soft mem-elements based on lipid interfaces and other biomolecules for applications in neuromorphic computing, autonomous structures and systems, and even adaptive brain-computer interfaces.