Nonlinear memristor model with exact solution allows for reservoir computing training and inference.

Memristive physical reservoir computing is a promising approach for solving data classification and temporal processing tasks. This method exploits the nonlinear dynamics of physical, low-power devices to achieve high-dimensional mapping of input signals. Ion-channel-based memristors, which operate with similar voltages, currents, and timescales as biological synapses, are promising due to their rich dynamics, especially for use in biological edge settings.

Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity.

Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space.

Author Correction: Artificial water channels enable fast and selective water permeation through water-wire networks.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Artificial water channels enable fast and selective water permeation through water-wire networks.

Artificial water channels are synthetic molecules that aim to mimic the structural and functional features of biological water channels (aquaporins). Here we report on a cluster-forming organic nanoarchitecture, peptide-appended hybrid[4]arene (PAH[4]), as a new class of artificial water channels. Fluorescence experiments and simulations demonstrated that PAH[4]s can form, through lateral diffusion, clusters in lipid membranes that provide synergistic membrane-spanning paths for a rapid and selective water permeation through water-wire networks.

Memristive plasticity in artificial electrical synapses via geometrically reconfigurable, gramicidin-doped biomembranes.

It is now known that mammalian brains leverage plasticity of both chemical and electrical synapses (ES) for collocating memory and processing. Unlike chemical synapses, ES join neurons via gap junction ion channels that permit fast, threshold-independent, and bidirectional ion transport. Like chemical synapses, ES exhibit activity-dependent plasticity, which modulates the ionic conductance between neurons and, thereby, enables adaptive synchronization of action potentials.

Author Correction: Dynamical nonlinear memory capacitance in biomimetic membranes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Dynamical nonlinear memory capacitance in biomimetic membranes.

Two-terminal memory elements, or memelements, capable of co-locating signal processing and memory via history-dependent reconfigurability at the nanoscale are vital for next-generation computing materials striving to match the brain's efficiency and flexible cognitive capabilities. While memory resistors, or memristors, have been widely reported, other types of memelements remain underexplored or undiscovered. Here we report the first example of a volatile, voltage-controlled memcapacitor in which capacitive memory arises from reversible and hysteretic geometrical changes in a lipid bilayer that mimics the composition and structure of biomembranes.

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes.

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.

The voltage-dependence of MscL has dipolar and dielectric contributions and is governed by local intramembrane electric field.

Channels without canonical voltage sensors can be modulated by voltage acting on other domains. Here we show that besides protein dipoles, pore hydration can be affected by electric fields. In patches, both WT MscL and its V23T mutant show a decrease in the tension midpoint with hyperpolarization.

Encapsulating Networks of Droplet Interface Bilayers in a Thermoreversible Organogel.

The development of membrane-based materials that exhibit the range and robustness of autonomic functions found in biological systems remains elusive. Droplet interface bilayers (DIBs) have been proposed as building blocks for such materials, owing to their simplicity, geometry, and capability for replicating cellular phenomena. Similar to how individual cells operate together to perform complex tasks and functions in tissues, networks of functionalized DIBs have been assembled in modular/scalable networks.

Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics.

Solid-state neuromorphic systems based on transistors or memristors have yet to achieve the interconnectivity, performance, and energy efficiency of the brain due to excessive noise, undesirable material properties, and nonbiological switching mechanisms. Here we demonstrate that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing. Unlike state-of-the-art devices, our two-terminal, biomolecular memristor features similar structure (biomembrane), switching mechanism (ion channels), and ionic transport modality as biological synapses while operating at considerably lower power.

Ferrofluid-Based Droplet Interface Bilayer Networks.

Droplet interface bilayer (DIB) networks allow for the construction of stimuli-responsive, membrane-based materials. Traditionally used for studying cellular transport phenomena, the DIB technique has proven its practicality when creating structured droplet networks. These structures consist of aqueous compartments capable of exchanging their contents across membranous barriers in a regulated fashion via embedded biomolecules, thus approximating the activity of natural cellular systems.

Multifunctional, Micropipette-based Method for Incorporation And Stimulation of Bacterial Mechanosensitive Ion Channels in Droplet Interface Bilayers.

MscL, a large conductance mechanosensitive channel (MSC), is a ubiquitous osmolyte release valve that helps bacteria survive abrupt hypo-osmotic shocks. It has been discovered and rigorously studied using the patch-clamp technique for almost three decades. Its basic role of translating tension applied to the cell membrane into permeability response makes it a strong candidate to function as a mechanoelectrical transducer in artificial membrane-based biomolecular devices.

Activation of bacterial channel MscL in mechanically stimulated droplet interface bilayers.

MscL, a stretch-activated channel, saves bacteria experiencing hypo-osmotic shocks from lysis. Its high conductance and controllable activation makes it a strong candidate to serve as a transducer in stimuli-responsive biomolecular materials. Droplet interface bilayers (DIBs), flexible insulating scaffolds for such materials, can be used as a new platform for incorporation and activation of MscL.