Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics.

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

Unravelling the operation of organic artificial neurons for neuromorphic bioelectronics.

Organic artificial neurons operating in liquid environments are crucial components in neuromorphic bioelectronics. However, the current understanding of these neurons is limited, hindering their rational design and development for realistic neuronal emulation in biological settings. Here we combine experiments, numerical non-linear simulations, and analytical tools to unravel the operation of organic artificial neurons. This comprehensive approach elucidates a broad spectrum of biorealistic behaviors, including firing properties, excitability, wetware operation, and biohybrid integration. The non-linear simulations are grounded in a physics-based framework, accounting for ion type and ion concentration in the electrolytic medium, organic mixed ionic-electronic parameters, and biomembrane features. The derived analytical expressions link the neurons spiking features with material and physical parameters, bridging closer the domains of artificial neurons and neuroscience. This work provides streamlined and transferable guidelines for the design, development, engineering, and optimization of organic artificial neurons, advancing next generation neuronal networks, neuromorphic electronics, and bioelectronics.

Aqueous chemimemristor based on proton-permeable graphene membranes.

Memristive devices, electrical elements whose resistance depends on the history of applied electrical signals, are leading candidates for future data storage and neuromorphic computing. Memristive devices typically rely on solid-state technology, while aqueous memristive devices are crucial for biology-related applications such as next-generation brain-machine interfaces. Here, we report a simple graphene-based aqueous memristive device with long-term and tunable memory regulated by reversible voltage-induced interfacial acid-base equilibria enabled by selective proton permeation through the graphene. Surface-specific vibrational spectroscopy verifies that the memory of the graphene resistivity arises from the hysteretic proton permeation through the graphene, apparent from the reorganization of interfacial water at the graphene/water interface. The proton permeation alters the surface charge density on the CaF2 substrate of the graphene, affecting graphene's electron mobility, and giving rise to synapse-like resistivity dynamics. The results pave the way for developing experimentally straightforward and conceptually simple aqueous electrolyte-based neuromorphic iontronics using two-dimensional (2D) materials.

Interfacing Biology and Electronics with Memristive Materials.

Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technological progress is opening application perspectives also in the biomedical field, particularly for implantable and lab-on-a-chip devices where advanced sensing technologies generate a large amount of data. Memristive devices are emerging as bioelectronic links merging biosensing with computation, acting as physical processors of analog signals or in the framework of advanced digital computing architectures. Recent developments in the processing of electrical neural signals, as well as on transduction and processing of chemical biomarkers of neural and endocrine functions, are reviewed. It is concluded with a critical perspective on the future applicability of memristive devices as pivotal building blocks in bio-AI fusion concepts and bionic schemes.

In Liquido Computation with Electrochemical Transistors and Mixed Conductors for Intelligent Bioelectronics.

Next-generation implantable computational devices require long-term-stable electronic components capable of operating in, and interacting with, electrolytic surroundings without being damaged. Organic electrochemical transistors (OECTs) emerged as fitting candidates. However, while single devices feature impressive figures of merit, integrated circuits (ICs) immersed in common electrolytes are hard to realize using electrochemical transistors, and there is no clear path forward for optimal top-down circuit design and high-density integration. The simple observation that two OECTs immersed in the same electrolytic medium will inevitably interact hampers their implementation in complex circuitry. The electrolyte's ionic conductivity connects all the devices in the liquid, producing unwanted and often unforeseeable dynamics. Minimizing or harnessing this crosstalk has been the focus of very recent studies. Herein, the main challenges, trends, and opportunities for realizing OECT-based circuitry in a liquid environment that could circumnavigate the hard limits of engineering and human physiology, are discussed. The most successful approaches in autonomous bioelectronics and information processing are analyzed. Elaborating on the strategies to circumvent and harness device crosstalk proves that platforms capable of complex computation and even machine learning (ML) can be realized in liquido using mixed ionic-electronic conductors (OMIECs).

A Reconfigurable Optoelectronic Synaptic Transistor with Stable Zr-CsPbI3 Nanocrystals for Visuomorphic Computing.

Reconfigurable phototransistor memory attracts considerable attention for adaptive visuomorphic computing, with highly efficient sensing, memory, and processing functions integrated onto a single device. However, developing reconfigurable phototransistor memory remains a challenge due to the lack of an all-optically controlled transition between short-term plasticity (STP) and long-term plasticity (LTP). Herein, an air-stable Zr-CsPbI3 perovskite nanocrystal (PNC)-based phototransistor memory is designed, which is capable of broadband photoresponses. Benefitting from the different electron capture ability of Zr-CsPbI3 PNCs to 650 and 405 nm light, an artificial synapse and non-volatile memory can be created on-demand and quickly reconfigured within a single device for specific purposes. Owing to the optically reconfigurable and wavelength-aware operation between STP and LTP modes, the integrated blue feature extraction and target recognition can be demonstrated in a homogeneous neuromorphic vision sensor array. This work suggests a new way in developing perovskite optoelectronic transistors for highly efficient in-sensor computing.

An artificial remote tactile device with 3D depth-of-field sensation.

Flexible tactile neuromorphic devices are becoming important as the impetus for the development of human-machine collaboration. However, accomplishing and further transcending human intelligence with artificial intelligence still confront many barriers. Here, we present a self-powered stretchable three-dimensional remote tactile device (3D-RTD) that performs the depth-of-field (DOF) sensation of external mechanical motions through a conductive-dielectric heterogeneous structure. The device can build a logic relationship precisely between DOF motions of an external active object and sensory potential signals of bipolar sign, frequency, amplitude, etc. The sensory mechanism is revealed on the basis of the electrostatic theory and multiphysics modeling, and the performance is verified via an artificial-biological hybrid system with micro/macroscale interaction. The feasibility of the 3D-RTD as an obstacle-avoidance patch for the blind is systematically demonstrated with a rat. This work paves the way for multimodal neuromorphic device that transcends the function of a biological one toward a new modality for brain-like intelligence.

High-Performance Bioelectronic Circuits Integrated on Biodegradable and Compostable Substrates with Fully Printed Mask-Less Organic Electrochemical Transistors.

Organic electrochemical transistors (OECTs) rely on volumetric ion-modulation of the electronic current to provide low-voltage operation, large signal amplification, enhanced sensing capabilities, and seamless integration with biology. The majority of current OECT technologies require multistep photolithographic microfabrication methods on glass or plastic substrates, which do not provide an ideal path toward ultralow cost ubiquitous and sustainable electronics and bioelectronics. At the same time, the development of advanced bioelectronic circuits combining bio-detection, amplification, and local processing functionalities urgently demand for OECT technology platforms with a monolithic integration of high-performance iontronic circuits and sensors. Here, fully printed mask-less OECTs fabricated on thin-film biodegradable and compostable substrates are proposed. The dispensing and capillary printing methods are used for depositing both high- and low-viscosity OECT materials. Fully printed OECT unipolar inverter circuits with a gain normalized to the supply voltage as high as 136.6 V-1 , and current-driven sensors for ion detection and real-time monitoring with a sensitivity of up to 506 mV dec-1 , are integrated on biodegradable and compostable substrates. These universal building blocks with the top-performance ever reported demonstrate the effectiveness of the proposed approach and can open opportunities for next-generation high-performance sustainable bioelectronics.

Organic neuromorphic electronics for sensorimotor integration and learning in robotics.

In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.

Solution-Processed Perovskite Field-Effect Transistor Artificial Synapses.

Metal halide perovskites are distinctive semiconductors that exhibit both ionic and electronic transport and are promising for artificial synapses. However, developing a 3-terminal transistor artificial synapse with the perovskite channel remains elusive due to the lack of a proper technique to regulate mobile ions in a non-volatile manner. Here, a solution-processed perovskite transistor is reported for artificial synapses through the implementation of a ferroelectric gate. The ferroelectric polarization provides a non-volatile electric field on the perovskite, leading to fixation of the mobile ions and hence modulation of the electronic conductance of the channel. Multi-state channel conductance is realized by partial ferroelectric polarization. The ferroelectric-gated perovskite transistor is successfully used as an artificial synapse that emulates basic synaptic functions such as long-term plasticity with excellent linearity, short-term as well as spike-timing-dependent plasticity. The strategy to regulate ion dynamics in the perovskites using the ferroelectric gate suggests a generic route to employ perovskites for synaptic electronics.

Current-Driven Organic Electrochemical Transistors for Monitoring Cell Layer Integrity with Enhanced Sensitivity.

In this progress report an overview is given on the use of the organic electrochemical transistor (OECT) as a biosensor for impedance sensing of cell layers. The transient OECT current can be used to detect changes in the impedance of the cell layer, as shown by Jimison et al. To circumvent the application of a high gate bias and preventing electrolysis of the electrolyte, in case of small impedance variations, an alternative measuring technique based on an OECT in a current-driven configuration is developed. The ion-sensitivity is larger than 1200 mV V-1 dec-1 at low operating voltage. It can be even further enhanced using an OECT based complementary amplifier, which consists of a p-type and an n-type OECT connected in series, as known from digital electronics. The monitoring of cell layer integrity and irreversible disruption of barrier function with the current-driven OECT is demonstrated for an epithelial Caco-2 cell layer, showing the enhanced ion-sensitivity as compared to the standard OECT configuration. As a state-of-the-art application of the current-driven OECT, the in situ monitoring of reversible tight junction modulation under the effect of drug additives, like poly-l-lysine, is discussed. This shows its potential for in vitro and even in vivo toxicological and drug delivery studies.

Multiscale real time and high sensitivity ion detection with complementary organic electrochemical transistors amplifier.

Ions are ubiquitous biological regulators playing a key role for vital processes in animals and plants. The combined detection of ion concentration and real-time monitoring of small variations with respect to the resting conditions is a multiscale functionality providing important information on health states. This multiscale functionality is still an open challenge for current ion sensing approaches. Here we show multiscale real-time and high-sensitivity ion detection with complementary organic electrochemical transistors amplifiers. The ion-sensing amplifier integrates in the same device both selective ion-to-electron transduction and local signal amplification demonstrating a sensitivity larger than 2300 mV V-1 dec-1, which overcomes the fundamental limit. It provides both ion detection over a range of five orders of magnitude and real-time monitoring of variations two orders of magnitude lower than the detected concentration, viz. multiscale ion detection. The approach is generally applicable to several transistor technologies and opens opportunities for multifunctional enhanced bioelectronics.

Probing the Impedance of a Biological Tissue with PEDOT:PSS-Coated Metal Electrodes: Effect of Electrode Size on Sensing Efficiency.

Electrodes coated with poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) have been employed to measure the integrity of cellular barriers. However, a systematic experimental study of the correlation between tissue integrity and impedance of the sensing device has not yet been conducted. Using impedance spectroscopy, how the impedance ratio of the biological tissue to the recording device affects the recording ability of the latter is investigated. PEDOT:PSS-coated electrodes of various dimensions are employed and the effect of their size to their sensing efficiency is examined. The biotic/abiotic ensemble is modeled with a simple equivalent circuit and an analytical expression of the total impedance as a function of frequency is extracted. The results reveal a critical impedance ratio of the biological tissue to the sensor which allows for efficient sensing of the tissue integrity. This work provides the ground rules for improved impedance-based biosensors with optimized sensitivity.

Monitoring of Cell Layer Integrity with a Current-Driven Organic Electrochemical Transistor.

The integrity of CaCo-2 cell barriers is investigated by organic electrochemical transistors (OECTs) in a current-driven configuration. Ion transport through cellular barriers via the paracellular pathway is modulated by tight junctions between adjacent cells. Rupturing its integrity by H2 O2 is monitored by the change of the output voltage in the transfer characteristics. It is demonstrated that by operating the OECT in a current-driven configuration, the sensitive and temporal resolution for monitoring the cell barrier integrity is strongly enhanced as compared to the OECT transient response measurement. As a result, current-driven OECTs are useful tools to assess dynamic and critical changes in tight junctions, relevant for clinical applications as drug targeting and screening.

Neuromorphic device architectures with global connectivity through electrolyte gating.

Information processing in the brain takes place in a network of neurons that are connected with each other by an immense number of synapses. At the same time, neurons are immersed in a common electrochemical environment, and global parameters such as concentrations of various hormones regulate the overall network function. This computational paradigm of global regulation, also known as homeoplasticity, has important implications in the overall behaviour of large neural ensembles and is barely addressed in neuromorphic device architectures. Here, we demonstrate the global control of an array of organic devices based on poly(3,4ethylenedioxythiophene):poly(styrene sulf) that are immersed in an electrolyte, a behaviour that resembles homeoplasticity phenomena of the neural environment. We use this effect to produce behaviour that is reminiscent of the coupling between local activity and global oscillations in the biological neural networks. We further show that the electrolyte establishes complex connections between individual devices, and leverage these connections to implement coincidence detection. These results demonstrate that electrolyte gating offers significant advantages for the realization of networks of neuromorphic devices of higher complexity and with minimal hardwired connectivity.

Orientation selectivity in a multi-gated organic electrochemical transistor.

UNLABELLED: Neuromorphic devices offer promising computational paradigms that transcend the limitations of conventional technologies. A prominent example, inspired by the workings of the brain, is spatiotemporal information processing. Here we demonstrate orientation selectivity, a spatiotemporal processing function of the visual cortex, using a poly(3,4ethylenedioxythiophene):poly(styrene sulfonate) ( PEDOT: PSS) organic electrochemical transistor with multiple gates. Spatially distributed inputs on a gate electrode array are found to correlate with the output of the transistor, leading to the ability to discriminate between different stimuli orientations. The demonstration of spatiotemporal processing in an organic electronic device paves the way for neuromorphic devices with new form factors and a facile interface with biology.

Neuromorphic Functions in PEDOT:PSS Organic Electrochemical Transistors.

Depressive short-term synaptic plasticity functions are implemented with a simple polymer poly(3,4ethylenedioxythiophene):poly(styrene sulfonate) ( PEDOT: PSS) organic electrochemical transistor device. These functions are a first step toward the realization of organic-based neuroinspired platforms with spatiotemporal information processing capabilities.