A Hippocampus-Inspired Dual-Gated Organic Artificial Synapse for Simultaneous Sensing of a Neurotransmitter and Light

Organic Mixed Ionic-Electronic Conductors for Bioelectronic Sensors: Materials and Operation Mechanisms

The field of organic mixed ionic-electronic conductors (OMIECs) has gained significant attention due to their ability to transport both electrons and ions, making them promising candidates for various applications. Initially focused on inorganic materials, the exploration of mixed conduction has expanded to organic materials, especially polymers, owing to their advantages such as solution processability, flexibility, and property tunability. OMIECs, particularly in the form of polymers, possess both electronic and ionic transport functionalities. This review provides an overview of OMIECs in various aspects covering mechanisms of charge transport including electronic transport, ionic transport, and ionic-electronic coupling, as well as conducting/semiconducting conjugated polymers and their applications in organic bioelectronics, including (multi)sensors, neuromorphic devices, and electrochromic devices. OMIECs show promise in organic bioelectronics due to their compatibility with biological systems and the ability to modulate electronic conduction and ionic transport, resembling the principles of biological systems. Organic electrochemical transistors (OECTs) based on OMIECs offer significant potential for bioelectronic applications, responding to external stimuli through modulation of ionic transport. An in-depth review of recent research achievements in organic bioelectronic applications using OMIECs, categorized based on physical and chemical stimuli as well as neuromorphic devices and circuit applications, is presented.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

An Ultrasensitive Biomimetic Optic Afferent Nervous System with Circadian Learnability

The optic afferent nervous system (OANS) plays a significant role in generating vision and circadian behaviors based on light detection and signals from the endocrine system. However, the bionic simulation of this photochemically mediated behavior is still a challenge for neuromorphic devices. Herein, stimuli of neurotransmitters at ultralow concentrations and illumination are coupled to artificial synapses with the aid of biofunctionalized heterojunction and tunneling to successfully simulate a circadian neural response. Furthermore, the mechanisms underlying the photosensitive synaptic current in response to stimuli are described. Interestingly, this OANS is demonstrated to be capable of mimicking normal and abnormal circadian learnability by combining the measured synaptic current with a three-layer spike neural network. Strong theoretical and experimental evidence, as well as applications, are provided for the proposed biomimetic OANS to demonstrate that it can reproduce biological circadian behavior, thus establishing it as a promising candidate for future neuromorphic intelligent robots.

PBDB-T/Pentacene-Based Organic Optoelectronic Synaptic Transistor with Adjustable Critical Flicker Fusion Frequency for Dynamic Vision

In the era of the Internet of Things and the rapid progress of artificial intelligence, there is a growing demand for advanced dynamic vision systems. Vision systems are no longer confined to static object detection and recognition, as the detection and recognition of moving objects are becoming increasingly important. To meet the requirements for more precise and efficient dynamic vision, the development of adaptive multimodal motion detection devices becomes imperative. Inspired by the varied response rates in biological vision, we introduce the concept of critical flicker fusion frequency (cFFF) and develop an organic optoelectronic synaptic transistor with adjustable cFFF. In situ Kelvin probe force microscopy analysis reveals that light signal recognition in this device originates from charge transfer in the poly[(2,6-(4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b:4,5-b']dithiophene)-co-(1,3-di(5-thiophene-2-yl)-5,7-bis(2-ethylhexyl)-benzo[1,2-c:4,5-c']dithiophene-4,8-dione)] (PBDB-T)/pentacene heterojunction, which can be effectively modulated by gate voltage. Building upon this, we implement different cFFF within a single device to facilitate the detection and recognition of objects moving at different speeds. This approach allows for resource allocation during dynamic detection, resulting in a reduction in power consumption. Our research holds great potential for enhancing the capabilities of dynamic visual systems.

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.

Towards Artificial Visual Sensory System: Organic Optoelectronic Synaptic Materials and Devices

Developing an artificial visual sensory system requires optoelectronic materials and devices that can mimic the behavior of biological synapses. Organic/polymeric semiconductors have emerged as promising candidates for optoelectronic synapses due to their tunable optoelectronic properties, mechanic flexibility, and biological compatibility. In this review, we discuss the recent progress in organic optoelectronic synaptic materials and devices, including their design principles, working mechanisms, and applications. We also highlight the challenges and opportunities in this field and provide insights into potential applications of these materials and devices in next-generation artificial visual systems. By leveraging the advances in organic optoelectronic materials and devices, we can envision its future development in artificial intelligence.

Donor-Acceptor Copolymers with 9-(2-Ethylhexyl)carbazole or Dibenzothiophene-5,5-dioxide Donor Units and 5,6-Difluorobenzo[c][1,2,5]thiadiazole Acceptor Units for Photonics

Semiconducting polymers, particularly of the third generation, including donor-acceptor (D-A) copolymers, are extensively studied due to their huge potential for photonic and electronic applications. Here, we report on two new D-A copolymers, CP1 and CP2, composed of different electron-donor (D) units: 9-(2-ethylhexyl)carbazole or dibenzothiophene-5,5-dioxide, respectively, and of 4,7-bis(4'-(2-octyldodecyl)thiophen-2'-yl)-5,6-difluorobenzo[c][1,2,5]thiadiazole building block with central 5,6-difluorobenzo[c][1,2,5]thiadiazole electron-acceptor (A) units, which were synthesized by Suzuki coupling in the high-boiling solvent xylene and characterized. The copolymers exhibited very good thermal and oxidation stability. A copolymer CP1 with different molecular weights was prepared in order to facilitate a comparison of CP1 with CP2 of comparable molecular weight and to reveal the relationship between molecular weight and properties. The photophysical, electrochemical, and electroluminescence properties were examined. Intense red photoluminescence (PL) with higher PL efficiencies for CP1 than for CP2 was observed in both solutions and films. Red shifts in the PL thin film spectra compared with the PL solution spectra indicated aggregate formation in the solid state. X-ray diffraction measurements revealed differences in the arrangement of molecules in thin films depending on the molecular weight of the copolymers. Light-emitting devices with efficient red emission and low onset voltages were prepared and characterized.

Inhibitory artificial synapses based on photoelectric co-modulation of graphene/WSe2van der Waals heterojunctions

Optical artificial synapses possess several advantages, including high bandwidth, strong interference immunity, and ultra-fast signal transmission, overcoming the limitations of electrically stimulated synapses. Among various functional materials, 2D materials exhibit exceptional optical and electrical properties. By utilizing van der Waals heterostructures formed by these materials through rational design, synaptic devices can mimic the information perception ability of biological systems. This lays the foundation for low-energy artificial vision systems and neuromorphic computing. This study introduces an inhibitory artificial synapse based on photoelectric co-modulation of graphene/WSe2van der Waals heterojunctions. By synergistically applying gate voltage and light pulses, we simulate memory and logic functions observed in the brain's visual cortex. We achieve the construction of inhibitory synapses, enabling properties such as postsynaptic current response, short-term and long-term plasticity, and paired-pulse facilitation. Additionally, we accomplish the inverse recovery of device conductivity through separate gate voltage stimulation. Through bidirectional modulation of the artificial synaptic conductance, we construct an artificial hardware neural network that achieves 92.5% accuracy in recognizing handwritten digital images from the MNIST dataset. The network also has good recognition accuracy for handwritten digital images with different standard deviation Gaussian noise applied and other datasets. Furthermore, we successfully mimic the neural behavior of aversive learning for alcohol withdrawal in alcoholic patients using the device properties. The promising capabilities of artificial synapses constructed through electrical and optical synergistic modulation make them suitable for wearable electronics and artificial vision systems.

Enhanced Multiwavelength Response of Flexible Synaptic Transistors for Human Sunburned Skin Simulation and Neuromorphic Computation

In biological species, optogenetics and bioimaging work together to regulate the function of neurons. Similarly, the light-controlled artificial synaptic system not only enhances computational speed but also simulates complex synaptic functions. However, reported synaptic properties are mainly limited to mimicking simple biological functions and single-wavelength responses. Therefore, the development of flexible synaptic devices with multiwavelength optical signal response and multifunctional simulation remains a challenge. Here, flexible organic light-stimulated synaptic transistors (LSSTs) enabled by alumina oxide (AlOX ), with a simple fabrication process, are reported. By embedding AlOX nanoparticles, the excitons separation efficiency is improved, allowing for multiple wavelength responses. Optimized LSSTs can respond to multiple optical and electrical signals in a highly synaptic manner. Multiwavelength optical synaptic plasticity, electrical synaptic plasticity, sunburned skin simulation, learning efficiency model controlled by photoelectric cooperative stimulation, neural network computing, "deer" picture learning and memory functions are successfully proposed, which promote the development for future artificial intelligent systems. Furthermore, as prepared flexible transistors exhibit mechanical flexibility with bending radius down to 2.5 mm and improved photosynaptic plasticity, which facilitating development of neuromorphic computing and multifunction integration systems at the device-level.

One-Phototransistor-One-Memristor Array with High-Linearity Light-Tunable Weight for Optic Neuromorphic Computing

The recent advances in optic neuromorphic devices have led to a subsequent rise in use for construction of energy-efficient artificial-vision systems. The widespread use can be attributed to their ability to capture, store, and process visual information from the environment. The primary limitations of existing optic neuromorphic devices include nonlinear weight updates, cross-talk issues, and silicon process incompatibility. In this study, a highly linear, light-tunable, cross-talk-free, and silicon-compatible one-phototransistor-one-memristor (1PT1R) optic memristor is experimentally demonstrated for the implementation of an optic artificial neural network (OANN). For optic image recognition in the experiment, an OANN is constructed using a 16 × 3 1PT1R memristor array, and it is trained on an online platform. The model yields an accuracy of 99.3% after only ten training epochs. The 1PT1R memristor, which shows good performance, demonstrates its ability as an excellent hardware solution for highly efficient optic neuromorphic and edge computing.

From Ferroelectric Material Optimization to Neuromorphic Devices

Due to the voltage driven switching at low voltages combined with nonvolatility of the achieved polarization state, ferroelectric materials have a unique potential for low power nonvolatile electronic devices. The competitivity of such devices is hindered by compatibility issues of well-known ferroelectrics with established semiconductor technology. The discovery of ferroelectricity in hafnium oxide changed this situation. The natural application of nonvolatile devices is as a memory cell. Nonvolatile memory devices also built the basis for other applications like in-memory or neuromorphic computing. Three different basic ferroelectric devices can be constructed: ferroelectric capacitors, ferroelectric field effect transistors and ferroelectric tunneling junctions. In this article first the material science of the ferroelectricity in hafnium oxide will be summarized with a special focus on tailoring the switching characteristics towards different applications.The current status of nonvolatile ferroelectric memories then lays the ground for looking into applications like in-memory computing. Finally, a special focus will be given to showcase how the basic building blocks of spiking neural networks, the neuron and the synapse, can be realized and how they can be combined to realize neuromorphic computing systems. A summary, comparison with other technologies like resistive switching devices and an outlook completes the paper.

Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration

The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing booming exploration, and is the stable building block for brain-like computing. Given that the storage and computing separation architecture of von Neumann construction is not conducive to the current explosive information processing, it is critical to accelerate the connection between hardware systems and software simulations of intelligent synapses. So far, various works based on a transistor-based synaptic system successfully simulated functions similar to biological nerves in the human brain. However, the influence of the semiconductor and the device structural design on synaptic properties is still poorly linked. This review concretely emphasizes the recent advances in the novel structure design of semiconductor materials and devices used in synaptic transistors, not only from a single multifunction synaptic device but also to system application with various connected routes and related working mechanisms. Finally, crises and opportunities in transistor-based synaptic interconnection are discussed and predicted.

Carbazole-Fluorene Copolymers with Various Substituents at the Carbazole Nitrogen: Structure-Properties Relationship

Carbazole derivatives, carbazole-containing polymers and iridium complexes are of interest due to many possible applications in photonics, electronics and biology, particularly as active or hole-transporting layers in organic as well as perovskite devices due to their interesting properties. Here, a series of carbazole-fluorene conjugated copolymers with various substituents at the N-carbazole position (2-methoxycarbonylethyl, 2-carboxyethyl, 2-ethylhexyl, and nonan-2,4-dionatoiridium(III)bis(2-phenylpyridine-N,C^2')-9-yl) was prepared by Suzuki coupling. Their photophysical, electrochemical and electroluminescence (EL) properties were studied. Effects of molecular weight and substituents attached to carbazole unit on their properties are reported. The carbazole-fluorene copolymers in dilute solutions exhibited intense photoluminescence (PL) emission in the blue spectral region with high PL quantum yields (78-87%) except for the copolymer with the iridium complex (23%). Similar PL spectra were observed in dilute solutions. More pronounced differences were found in thin film PL and EL properties due to excimer/aggregate formation. Light-emitting devices (LEDs) made of copolymers with 2-ethylhexyl as N-carbazole substituent exhibited efficient EL emission with the best performance and the lowest EL onset voltages (3-4 V), while the LEDs made of copolymers with other substituents were not as efficient, but showed anomalous behavior and memory effects in current-voltage characteristics promising also for bio-inspired electronics.

Organic multilevel (opto)electronic memories towards neuromorphic applications

In the past decades, neuromorphic computing has attracted the interest of the scientific community due to its potential to circumvent the von Neumann bottleneck. Organic materials, owing to their fine tunablility and their ability to be used in multilevel memories, represent a promising class of materials to fabricate neuromorphic devices with the key requirement of operation with synaptic weight. In this review, recent studies of organic multilevel memory are presented. The operating principles and the latest achievements obtained with devices exploiting the main approaches to reach multilevel operation are discussed, with emphasis on organic devices using floating gates, ferroelectric materials, polymer electrets and photochromic molecules. The latest results obtained using organic multilevel memories for neuromorphic circuits are explored and the major advantages and drawbacks of the use of organic materials for neuromorphic applications are discussed.

Flexibly Photo-Regulated Brain-Inspired Functions in Flexible Neuromorphic Transistors

As an attractive prototype for neuromorphic computing, the difficultly attained three-terminal platforms have specific advantages in implementing the brain-inspired functions. Also, in these devices, the most utilized mechanisms are confined to the electrical gate-controlled ionic migrations, which are sensitive to the device defects and stoichiometric ratio. The resultant memristive responses have fluctuant characteristics, which have adverse influences on the neural emulations. Herein, we designed a specific transistor platform with light-regulated ambipolar memory characteristics. Also, based on its gentle processes of charge trapping, we obtain the impressive memristive performances featured by smooth responses and long-term endurable characteristics. The optoelectronic samples were also fabricated on flexible substrates successfully. Interestingly, based on the optoelectronic signals of the flexible devices, we endow the desirable optical processes with the brain-inspired emulations. We can flexibly emulate the light-inspired learning-memory functions in a synapse and further devise the advanced synapse array. More importantly, through this versatile platform, we investigate the mutual regulation of excitation and inhibition and implement their sensitive-mode transformations and the homeostasis property, which is conducive to ensuring the stability of overall neural activity. Furthermore, our flexible optoelectronic platform achieves high classification accuracy when implemented in artificial neural network simulations. This work demonstrates the advantages of the optoelectronic platform in implementing the significant brain-inspired functions and provides an insight into the future integration of visible sensing in flexible optoelectronic transistor platforms.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Synaptic and Gradual Conductance Switching Behaviors in CeO2/Nb-SrTiO3 Heterojunction Memristors for Electrocardiogram Signal Recognition

The synaptic properties of memristors have been widely studied. However, researchers are still committed to solving various challenges, including the study of highly reliable memristors with comprehensive synaptic functions and memristors that simulate highly complex neurological learning rules. In this work, we report a CeO₂/Nb-SrTiO₃ heterojunction memristor whose conductance could be gradually tuned under both positive and negative pulse trains. Due to the gradual conductance switching behavior and the high switching ratio (10⁵), the CeO₂/Nb-SrTiO₃ heterojunction memristor could dutifully mimic biosynaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation and depression (PPF/PPD), spike amplitude-dependent plasticity (SADP), spike duration-dependent plasticity (SDDP), spike rate-dependent plasticity (SRDP), paired/triplet spiking-time-dependent plasticity (STDP), and Bienenstock-Cooper-Munro (BCM) rules. Moreover, a convolutional neural network based on the memristors is constructed to identify the electrocardiogram (ECG) data sets to realize the diagnosis of diseases with a recognition accuracy of 93%. Besides, the recognition accuracy of the handwriting digit reaches 96%. These studies broaden the research scope of high-level synaptic behavior and lay a foundation for the future full synaptic memristor networks.

Multiplexed Complementary Signal Transmission for a Self-Regulating Artificial Nervous System

Neuromorphic engineering has emerged as a promising research field that can enable efficient and sophisticated signal transmission by mimicking the biological nervous system. This paper presents an artificial nervous system capable of facile self-regulation via multiplexed complementary signals. Based on the tunable nature of the Schottky barrier of a complementary signal integration circuit, a pair of complementary signals is successfully integrated to realize efficient signal transmission. As a proof of concept, a feedback-based blood glucose level control system is constructed by incorporating a glucose/insulin sensor, a complementary signal integration circuit, an artificial synapse, and an artificial neuron circuit. Certain amounts of glucose and insulin in the initial state are detected by each sensor and reflected as positive and negative amplitudes of the multiplexed presynaptic pulses, respectively. Subsequently, the pulses are converted to postsynaptic current, which triggered the injection of glucose or insulin in a way that confined the glucose level to a desirable range. The proposed artificial nervous system demonstrates the notable potential of practical advances in complementary control engineering.

Wavelength and Polarization Sensitive Synaptic Phototransistor Based on Organic n-type Semiconductor/Supramolecular J-Aggregate Heterostructure

Human retina- and brain-inspired optoelectronic synapses, which integrate light detection and signal memory functions for data processing, have significant interest because of their potential applications for artificial vision technology. In nature, many animals such as mantis shrimp use polarized light information as well as scalar information including wavelength and intensity; however, a spectropolarimetric organic optoelectronic synapse has been seldom investigated. Herein, we report an organic synaptic phototransistor, consisting of a charge trapping liquid-crystalline perylene bisimide J-aggregate and a charge transporting crystalline dichlorinated naphthalene diimide, that can detect both wavelength and polarization information. The device shows persistent positive and negative photocurrents under low and high voltage conditions, respectively. Furthermore, the aligned organic heterostructure in the thin-film enables linearly polarized light to be absorbed with a dichroic ratio of 1.4 and 3.7 under transverse polarized blue and red light illumination, respectively. These features allow polarized light sensitive postsynaptic functions in the device. Consequently, a simple polarization imaging sensor array is successfully demonstrated using photonic synapses, which suggests that a supramolecular material is an important candidate for the development of spectropolarimetric neuromorphic vision systems.

Programmable ferroelectric bionic vision hardware with selective attention for high-precision image classification

Selective attention is an efficient processing strategy to allocate computational resources for pivotal optical information. However, the hardware implementation of selective visual attention in conventional intelligent system is usually bulky and complex along with high computational cost. Here, programmable ferroelectric bionic vision hardware to emulate the selective attention is proposed. The tunneling effect of photogenerated carriers are controlled by dynamic variation of energy barrier, enabling the modulation of memory strength from 9.1% to 47.1% without peripheral storage unit. The molecular polarization of ferroelectric P(VDF-TrFE) layer enables a single device not only multiple nonvolatile states but also the implementation of selective attention. With these ferroelectric devices are arrayed together, UV light information can be selectively recorded and suppressed the with high current decibel level. Furthermore, the device with positive polarization exhibits high wavelength dependence in the image attention processing, and the fabricated ferroelectric sensory network exhibits high accuracy of 95.7% in the pattern classification for multi-wavelength images. This study can enrich the neuromorphic functions of bioinspired sensing devices and pave the way for profound implications of future bioinspired optoelectronics.

Tuning Ambipolarity of the Conjugated Polymer Channel Layers of Floating-Gate Free Transistors: From Volatile Memories to Artificial Synapses

Three-terminal synaptic transistor has drawn significant research interests for neuromorphic computation due to its advantage of facile device integrability. Lately, bulk-heterojunction-based synaptic transistors with bipolar modulation are proposed to exempt the use of an additional floating gate. However, the actual correlation between the channel's ambipolarity, memory characteristic, and synaptic behavior for a floating-gate free transistor has not been investigated yet. Herein, by studying five diketopyrrolopyrrole-benzotriazole dual-acceptor random conjugated polymers, a clear correlation among the hole/electron ratio, the memory retention characteristic, and the synaptic behavior for the polymer channel layer in a floating-gate free transistor is described. It reveals that the polymers with balanced ambipolarity possess better charge trapping capabilities and larger memory windows; however, the high ambipolarity results in higher volatility of the memory characteristics, namely poor memory retention capability. In contrast, the polymer with a reduced ambipolarity possesses an enhanced memory retention capability despite showing a reduced memory window. It is further manifested that this enhanced charge retention capability enables the device to present artificial synaptic characteristics. The results highlight the importance of the channel's ambipolarity of floating-gate free transistors on the resultant volatile memory characteristics and synaptic behaviors.

Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics

Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.

A flexible dual-gate hetero-synaptic transistor for spatiotemporal information processing

Artificial synapses based on electrolyte gated transistors with conductance modulation characteristics have demonstrated their great potential in emulating the memory functions in the human brain for neuromorphic computing. While previous studies are mostly focused on the emulation of the basic memory functions of homo-synapses using single-gate transistors, multi-gate transistors offer opportunities for the mimicry of more complex and advanced memory formation behaviors in biological hetero-synapses. In this work, we demonstrate an artificial hetero-synapse based on a dual-gate electrolyte transistor that can implement in situ spatiotemporal information integration and storage. We show that electric pulses applied on a single gate or unsynchronized electric pulses applied on dual gates only induce volatile conductance modulation for short-term memory emulation. In contrast, the device integrates the electric pulses coincidently applied on the dual gates in a supralinear manner and exhibits nonvolatile conductance modulation, enabling long-term memory emulation. Further studies prove that artificial neural networks based on such hetero-synaptic transistors can autonomously filter the random noise signals in the dual-gate inputs during spatiotemporal integration, facilitating the formation of accurate and stable memory. Compared to the single-gate synaptic transistor, the classification accuracy of MNIST handwritten digits using the hetero-synaptic transistor is improved from 89.3% to 99.0%. These findings demonstrate the great potential of multi-gate hetero-synaptic transistors in simulating complex spatiotemporal information processing functions and provide new platforms for the design of advanced neuromorphic computing systems.

Photo-induced non-volatile VO2 phase transition for neuromorphic ultraviolet sensors

In the quest for emerging in-sensor computing, materials that respond to optical stimuli in conjunction with non-volatile phase transition are highly desired for realizing bioinspired neuromorphic vision components. Here, we report a non-volatile multi-level control of VO₂ films by oxygen stoichiometry engineering under ultraviolet irradiation. Based on the reversible regulation of VO₂ films using ultraviolet irradiation and electrolyte gating, we demonstrate a proof-of-principle neuromorphic ultraviolet sensor with integrated sensing, memory, and processing functions at room temperature, and also prove its silicon compatible potential through the wafer-scale integration of a neuromorphic sensor array. The device displays linear weight update with optical writing because its metallic phase proportion increases almost linearly with the light dosage. Moreover, the artificial neural network consisting of this neuromorphic sensor can extract ultraviolet information from the surrounding environment, and significantly improve the recognition accuracy from 24% to 93%. This work provides a path to design neuromorphic sensors and will facilitate the potential applications in artificial vision systems.

Bioinspired neuromorphic vision components are highly desired for the emerging in-sensor computing technology. Here, Ge et al. develop an array of optoelectronic synapses capable of memorizing and processing ultraviolet images facilitated by photo-induced non-volatile phase transition in VO₂ films.

Bionanohybrid composed of metalloprotein/DNA/MoS2/peptides to control the intracellular redox states of living cells and its applicability as a cell-based biomemory device

The development of cell-based bioelectronic devices largely depends on the direct control of intracellular redox states. However, most related studies have focused on the accurate measurement of electrical signals from living cells, whereas direct intracellular state control remains largely unexplored. Here, we developed a biocompatible transmembranal bionanohybrid structure composed of a recombinant metalloprotein, DNA, molybdenum disulfide nanoparticles (MoS₂), and peptides to control intracellular redox states, which can be used as a cell-based biomemory device. Using the capacitance of MoS₂ located inside the cell, the bionanohybrid controled the intracellular redox states of living cells by recording and extracting intracellular charges, which inturn was achieved by activating (writing) and deactivating (erasing) the cells. As a proof of concept, cell-based biomemory functions including writing, reading, and erasing were successfully demonstrated and confirmed via electrochemical methods and patch-clamp analyses, resulting in the development of the first in vitro cell-based biomemory device. This newly developed bionanohybrid provides a novel approach to control cellular redox states for cell-based bioelectronic applications, and can be applicable in a wide range of biological fields including bioelectronic medicine and intracellular redox status regulation.

Advances in materials and devices for mimicking sensory adaptation

Adaptive devices, which aim to adjust electrical behaviors autonomically to external stimuli, are considered to be attractive candidates for next-generation artificial perception systems. Compared with typical electronic devices with stable signal output, adaptive devices possess unique features in exhibiting dynamic fitness to varying environments. To meet this requirement, increasing efforts have been made focusing on developing new materials, functional interfaces and novel device geometry for sensory perception applications. In this review, we summarize the recent advances in materials and devices for mimicking sensory adaptation. Keeping this in mind, we first introduce the fundamentals of biological sensory adaptation. Thereafter, the recent progress in mimicking sensory adaptation, such as tactile and visual adaptive systems, is overviewed. Moreover, we suggest five strategies to construct adaptive devices. Finally, challenges and perspectives are proposed to highlight the directions that deserve focused attention in this flourishing field.

The Flow of Axonal Information Among Hippocampal Subregions: 1. Feed-Forward and Feedback Network Spatial Dynamics Underpinning Emergent Information Processing

The tri-synaptic pathway in the mammalian hippocampus enables cognitive learning and memory. Despite decades of reports on anatomy and physiology, the functional architecture of the hippocampal network remains poorly understood in terms of the dynamics of axonal information transfer between subregions. Information inputs largely flow from the entorhinal cortex (EC) to the dentate gyrus (DG), and then are processed further in the CA3 and CA1 before returning to the EC. Here, we reconstructed elements of the rat hippocampus in a novel device over an electrode array that allowed for monitoring the directionality of individual axons between the subregions. The direction of spike propagation was determined by the transmission delay of the axons recorded between two electrodes in microfluidic tunnels. The majority of axons from the EC to the DG operated in the feed-forward direction, with other regions developing unexpectedly large proportions of feedback axons to balance excitation. Spike timing in axons between each region followed single exponential log-log distributions over two orders of magnitude from 0.01 to 1 s, indicating that conventional descriptors of mean firing rates are misleading assumptions. Most of the spiking occurred in bursts that required two exponentials to fit the distribution of inter-burst intervals. This suggested the presence of up-states and down-states in every region, with the least up-states in the DG to CA3 feed-forward axons and the CA3 subregion. The peaks of the log-normal distributions of intra-burst spike rates were similar in axons between regions with modes around 95 Hz distributed over an order of magnitude. Burst durations were also log-normally distributed around a peak of 88 ms over two orders of magnitude. Despite the diversity of these spike distributions, spike rates from individual axons were often linearly correlated to subregions. These linear relationships enabled the generation of structural connectivity graphs, not possible previously without the directional flow of axonal information. The rich axonal spike dynamics between subregions of the hippocampus reveal both constraints and broad emergent dynamics of hippocampal architecture. Knowledge of this network architecture may enable more efficient computational artificial intelligence (AI) networks, neuromorphic hardware, and stimulation and decoding from cognitive implants.