Biological Sensory Systems Research Articles

Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges.

Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.

Wirelessly detecting of spatiotemporal mechanical stimuli by a bio-inspired neural sensor via temperature signals

Traditional artificial sensory systems, inspired by biological sensory nervous systems, require the integration of distributed units and wired connections to realize the perception of external mechanical stimuli, which brings disadvantages such as complex connections, complicated structures, and interference between signals. Herein, we propose a bio-inspired artificial neural sensor with a highlighted ability of wirelessly detecting spatiotemporal mechanical stimuli, which is fabricated by carbon nanotube and polymer composites. Because the resistance of the sensor varies in respond to external mechanical stimuli, the consequential temperature changes caused by Joule heating effect can be wirelessly detected, realizing the recognition of different mechanical stimuli and multi-positional sensing. The great durability (>1400 cycles) allow the sensor to withstand long-lasting mechanical stimuli. In addition, the artificial neural sensor enables programmable design in device shapes and functions, and it needs no energy consumption without stimulus. We demonstrate a series of touch-interaction applications based on the neural sensor, such as human–computer interaction, encrypted information transmission, and remote control. We hope that this artificial neural sensor will facilitate the development of complex bionic sensing systems, virtual reality, and metaverse.

Mimicking Pain Conditioning Using an Electrolyte‐Gated Organic Synaptic Transistor

AbstractPain‐perceptual Nociceptors (PPNs) in the biological sensory system are critical for the human body to perceive noxious stimuli from the outside world and start appropriate motor responses to minimize potential physical damage. Realization artificial nociceptors with unique synaptic and pain‐perceptual functions of the human nervous system is a key step to develop the next‐generation artificial intelligence systems. Here, an artificial pain‐perceptual device based on an ionic liquid‐polymer solid‐state electrolyte‐gated poly(3‐hexylthiophene) (P3HT) transistor is reported. With the drift motion of mobile ions in the electrolyte layer and the formation of an electric double layer (EDL) at electrolyte/semiconductor interface under an applied electric field, the channel conductance can be tuned progressively to exhibit synaptic weight modulation function. On top of the artificial synaptic functions, the P3HT/electrolyte‐based transistor successfully emulates the pain‐perception process in a biological nociceptor, with all unique features of threshold, no adaptation, sensitization/desensitization, and relaxation. Moreover, the pain conditioning process (i.e., hyperalgesic response, and pain extinction effect) through associative learning is demonstrated using the artificial synaptic transistor, indicating its potential application in bio‐inspired neural systems.

Maskless Femtosecond‐Laser‐Processed Ionotronic Double‐Gate Transistor Array for Pattern Adaptation Emulation

AbstractSensory adaptation is a very important function of the biological nervous system to maintain survival. It helps us to better perceive, understand, and adapt to the environment. Therefore, building artificial sensory adaptation systems through neuromorphic devices is a goal that humans have been continuously pursuing. Unfortunately, current research has either based on individual adaptive devices or employed complex processes. At present, a facile fabrication of adaptive device arrays is still a great challenge. Herein, a maskless ionotronic double‐gate oxide transistor array with pattern adaptation function is demonstrated using femtosecond laser process. Such process is a maskless patterning technology and free of chemical contamination, enabling a facile integration of device arrays. The as‐fabricated transistor exhibits a low subthreshold swing of 265 mV dec−1 and a reasonable current switching ratio of ≈103 under a low operating voltage of 1 V. Interestingly, it can achieve a transition from nonadaptive to adaptive behavior by the other gate terminal. Moreover, the desensitization behavior after adaptation can be realized for mimicking the self‐protection capability of biological sensory systems. Finally, a 2 × 2 transistor array is demonstrated for emulating the experience‐dependent pattern adaptation ability in the human brain. Therefore, this adaptive device array using femtosecond laser process may provide a good opportunity for the artificial sensory adaptation systems and the next‐generation bionic robots.

A habituation memristor model for lung cancer screening application

—Lung cancer screening is critical to the diagnosis and treatment of patients. Today, computed tomography (CT) scanning technology provides a promising approach for the screening of lung cancer. Nevertheless, the redundant information in CT images often limits the efficiency and accuracy of screening. The biological sensory nervous system has an important mechanism for screening out redundant information, namely habituation. Here, we designed a second-order memristor model with habituation characteristics. Some of the habituation behavior of the memristor model has been demonstrated with LTspice simulation. Furthermore, the habituation memristor model is incorporated in a volatile memristor based leaky integrate and fire (LIF) neuron circuit to construct a simple neural system. The simulation results indicate that the neural system exhibits reliable habituation behaviors. Finally, lung cancer screening tasks have been implemented based on the neural system with habituation behavior. The habituation memristor circuit serves as a data preprocessing layer that filters out relevant information from lung cancer images. The results indicate that the performance and accuracy of lung cancer screening performance are noticeably better than the neural system without habituation behavior. This work provides a new idea for lung cancer screening implementation.

Photonic Synapses for Image Recognition and High Density Integration of Simplified Artificial Neural Networks

AbstractWith the rapid development of artificial intelligence (AI), there is an urgent need for developing a biological sensory perception system that can simulate the human brain for information processing. Inspired by the biological vision system, photo‐responsive photonic synapses are ideal devices for constructing photosensitive artificial neural networks for neuromorphic computing tasks. This paper reports a stable photonic synaptic device in an array layout with adjustable synaptic plasticity under ultraviolet light pulses. Since the heterojunction has a photoconductivity effect and the trap layer provides superior charge carrier trapping capability, optical sensing, memory, and neuromorphic computing are integrated into a single device. Meanwhile, supervised learning of handwritten digitals is achieved by exploiting the multistate conductance by photoelectric co‐modulation and the specific decay law. The recognition rate reaches 90.6% and hardly changes with time. Additionally, the device can simplify the artificial neural network (ANN) and reduce its size to 3.78% of the original network while retaining strong fault tolerance and learning ability. The photonic artificial synapses based on ultraviolet light modulation provide a novel and effective approach for photosensory ANNs to perform in situ computation.

Spider-inspired tunable mechanosensor for biomedical applications

The recent advances of wearable sensors are remarkable but there are still limitations that they need to be refabricated to tune the sensor for target signal. However, biological sensory systems have the inherent potential to adjust their sensitivity according to the external environment, allowing for a broad and enhanced detection. Here, we developed a Tunable, Ultrasensitive, Nature-inspired, Epidermal Sensor (TUNES) that the strain sensitivity was dramatically increased (GF ~30k) and the pressure sensitivity could be tuned (10–254 kPa−1) by preset membrane tension. The sensor adjusts the sensitivity to the pressure regime by preset tension, so it can measure a wide range (0.05 Pa–25 kPa) with the best performance: from very small signals such as minute pulse to relatively large signals such as muscle contraction and respiration. We verified its capabilities as a wearable health monitoring system by clinical trial comparing with pressure wire which is considered the current gold standard of blood pressure (r = 0.96) and home health care system by binary classification of Old’s/Young’s pulse waves via machine learning (accuracy 95%).

Neuromorphic Gustatory System with Salt-Taste Perception, Information Processing, and Excessive-Intake Warning Capabilities.

Emulation of the process of a biological gustatory system could benefit the reconstruction of sense of taste. Here we demonstrate the first neuromorphic gustatory system that emulates the ability of taste perception, information processing, and excessive-intake warning functions. The system integrates a chitosan-derived ion-gel sensor, SnO2 nanowire artificial synapses, and an effect-executive unit. The system accomplish perception and encoding behaviors for taste stimulation without using complex circuits and multivariate analysis, showing short response delay (<1 s), long taste memory duration (>2 h), and a wide perceptive concentration range (0.02-6 wt % salt solution). Especially, SnO2 NW artificial synapses have extremely small response voltage (1 mV), exceeding the biological level by orders of magnitude, representing so-far the highest sensitivity record. This work provides a promising strategy to develop bioinspired and biointegrated electronics with the intention of mimicking and restoring the functions of biological sensory systems.

Nonequilibrium dynamics of adaptation in sensory systems.

Adaptation is used by biological sensory systems to respond to a wide range of environmental signals, by adapting their response properties to the statistics of the stimulus in order to maximize information transmission. We derive rules of optimal adaptation to changes in the mean and variance of a continuous stimulus in terms of Bayesian filters and map them onto stochastic equationsthat couple the state of the environment to an internal variable controlling the response function. We calculate numerical and exact results for the speed and accuracy of adaptation and its impact on information transmission. We find that, in the regime of efficient adaptation, the speed of adaptation scales sublinearly with the rate of change of the environment. Finally, we exploit the mathematical equivalence between adaptation and stochastic thermodynamics to quantitatively relate adaptation to the irreversibility of the adaptation time course, defined by the rate of entropy production. Our results suggest a means to empirically quantify adaptation in a model-free and nonparametric way.

Detection of The Deaf Signal Language Using The Single Shot Detection (SSD) Method

Sign Language is a language that prioritizes manual communication, body language, and lip movements, instead of sound, to communicate. Deaf people are the main group who use this language, usually by combining hand shape, orientation and movement of the hands, arms, and body, and facial expressions to express their thoughts. Therefore, the researcher created an image recognition program in sign language using the Single Shot Detection (SSD) method, which is a convolution activity by combining several layers of preparation, by utilizing several components that move together and are motivated by a biological sensory system. The letters used in making sign language programs use the letters of the alphabet (az). This sign language detection programming that runs on the Google Collaboratory application

A Bio‐Inspired Neuromorphic Sensory System

The advent of the intelligent society leads to the exponential growth of information, imposing urgent requirements in a time‐ and energy‐efficient way to process information where data are generated. This issue can be addressed by the neuromorphic paradigm of computing inspired by biological sensory systems that build up the association between external stimuli and the response of an organism in real‐time; in the paradigm, a neuromorphic system is integrated with sensors to form an artificial sensory system. Herein, a neuromorphic sensory system with integrated capabilities of gas sensing, data storage, and processing is demonstrated. Leaky integrate‐and‐fire (LIF) neurons, the basic computing units in the system, are realized with volatile memristive device Pt/Ag/TaOx/Pt; sensory neurons, i.e., the LIF neurons connected with an array of gas sensors, detect gases and convert the chemical information of gases into neural spikes; synapses based on nonvolatile memristive device Pt/Ta/TaOx/Pt transmit the signals from sensory neurons to relay neurons according to synaptic weights, which are trained by the supervised spike‐rate dependent plasticity; relay neurons then process the signals from the synapses and classify gases. The approach of this work can also be applied to emulate other biological perceptions through the integration with different sensors.

Ultralow‐Power and Multisensory Artificial Synapse Based on Electrolyte‐Gated Vertical Organic Transistors

AbstractBioinspired electronics have shown great potential in the field of artificial intelligence and brain‐like science. Low energy consumption and multifunction are key factors for its application. Here, multisensory artificial synapse and neural networks based on electrolyte‐gated vertical organic field‐effect transistors (VOFETs) are first developed. The channel length of the organic transistor is scaled down to 30 nm through cross‐linking strategy. Owing to the short channel length and extremely large capacitance of the electric double layer formed at the electrolyte–channel interface, the minimum power consumption of one synaptic event is 0.06 fJ, which is significantly lower than that required by biological synapses (1–10 fJ). Moreover, the artificial synapse can be trained to learn and memory images in a 5 × 5 synapse array and emulate the human brain's spatiotemporal information processing and sound azimuth detection. Finally, the artificial tongue is designed using the synaptic transistor that can discriminate acidity. Overall, this study provides new insights into realizing energy‐efficient artificial synapses and mimicking biological sensory systems.

Multimode modulated memristors for in-sensor computing system

To develop future interactive artificial intelligence system, the construction of high-performance human perception system and processing system is vital. In a traditional perceptual and processing system, sensors, memory and processing units are physically separated because of their different functions and manufacture conditions, which results in frequent shuttling and format transformation of data resulting in long time delay and high energy consumption. Inspired by biological sensory nervous system, one has proposed the concept of in-sensor computing system in which the basic unit integrates sensor, storage and computing functions in the same place. In-sensor computing technology can provide a reliable technical scheme for the area of sensory processing. Artificial memristive synapse capable of sensing light, pressure, chemical substances, etc. is one type of ideal device for the application of in-sensor computing system. In this paper, at the device level, recent progress of sensory memristive synapses applied to in-sensor computing systems are reviewed, including visual, olfactory, auditory, tactile and multimode sensation. This review points out the challenge and prospect from the aspects of device, fabrication, integrated circuit system architecture and algorithms, aiming to provide possible research direction for future development of in-sensor computing system.

Neuromorphic behaviors of N-type locally-active memristor

Owing to the advantages of high integration, low power consumption and locally active characteristics, locally-active memristor (LAM) has shown great potential applications in neuromorphic computing. To further investigate the neuromorphic dynamics of LAMs, a simple N-type LAM mathematical model is proposed in this work. By analyzing its voltage-current characteristic and small-signal equivalent circuit, a neuron circuit based on the N-type LAM is designed, where a variety of neuromorphic behaviors are successfully simulated, such as “all-or-nothing” behavior, spikes, bursting, periodic oscillation, etc. Moreover, Hopf bifurcation theory and numerical analysis method are used to study the dynamics of the circuit quantitatively. Then, an artificial tactile neuron and its frequency characteristics are presented by using the proposed neuron circuit topology. The simulation results show that when the amplitude of the input signal is lower than the threshold, the oscillation frequency of the output signal of the artificial neuron circuit is positively correlated with the intensity of the input signal, and reaches a maximum value at the threshold. The above frequency characteristics are consistent with those of the exciting state of biological sensory system. Subsequently, if the incentive intensity continues to increase, the oscillation frequency will gradually decrease, corresponding to the protective inhibition behavior. Finally, the physical circuit of the N-type LAM, and artificialneuron circuit are realized. The experimental results accord well with the simulation results and theoreticalanalyses, manifesting the practicability of the N-type LAM model and the feasibility of artificial neuron circuit.

An artificial olfactory system with sensing, memory and self-protection capabilities

Artificial olfactory system has been received increasing attention due to its important applications in future artificial nose, humanoid robot and next generation human-computer interface. However, it is still a challenge to completely simulate the functions of the human olfactory system recognize, remember and induce muscle movement from pungent odor. Here, we have developed a flexible artificial olfactory system by integrating Sr-ZnO-based gas sensors, HfO x -based memristors and electrochemical actuators, which can recognize and memorize and make the self-protection action for NH 3 . The Sr-ZnO gas sensor can perceive and recognize NH 3 through the change of resistance, and the signal transferred to the memristor can switch the resistance states for the storage of ammonia information. Meanwhile, the activation of memristor can also trigger the movement of the electrochemical actuator to block the gas flow channel analogous to the self-protection action of covering the nose with the hand when smelling irritating NH 3 . Our artificial olfactory system provides a promising strategy for future bioinspired electronics research, especially in the field of mimicking biological sensory systems. A flexible artificial olfactory system integrating Sr-ZnO-based gas sensor, HfO x -based memristor, and an electrochemical actuator, which can recognize, memorize and make self-protection action for the NH 3 simulation. • An integrated artificial olfactory system was designed and fabricated. • The system realizes multiple functions by simply integrating flexible gas sensors, memristors and actuators. • The olfactory system realizes the perception, recognition, memory and action response of ammonia in the bionic nose.

Ion‐Gated Transistor: An Enabler for Sensing and Computing Integration

With the rapid development of the Internet of Things, the amount of data we involved in our daily life is growing exponentially, which poses significant challenges for data processing and transmission to the conventional terminal sensors that passively acquire external data. Inspired by biological sensory nervous systems, building artificial intelligent sensory systems with both sensing and computing capability is regarded as a promising way to address these challenges, by which the acquired data can be preprocessed locally and timely before transmitting them to the remote server for further processing. Ion‐gated transistors (IGTs), which have been widely used in sensors and have been recently investigated for neuromorphic computing, exhibit great potential in this domain. Herein, the essential operation principles, device structures, and electrical characteristics of IGT are introduced, and the recent developments in biosensors, neuromorphic computing, and intelligent sensors with near‐sensor computing and in‐sensor computing modes are summarized. To conclude, the current challenges and future development of IGT for intelligent sensory systems are presented.

Flexible GaN microwire-based piezotronic sensory memory device

Skin-inspired electronic devices that can store and retain impressions of sensory information after the removal of external stimuli are showing great significance for artificial sensory systems. Here, a single GaN microwire-based piezotronic sensory memory device (SMD) is presented to sense and memorize the impressions of tactile information. The SMD is capable to be programmed into a high resistance state by inputting external strain, and reversibly erased back to the low resistance state with an electrical voltage. Due to the piezotronic effect, the piezo-potential induced by compressive strain would cause the dissolution/redistribution of conductive channels of nitrogen vacancies in the bamboo-shaped GaN microwire. Furthermore, the SMD array demonstrates a distinct spatial mapping of external strain sensing and retaining with the operations of strain program and electrical erase. The single micro/nanowire-based sensory memory device will have great applications in the field of tactile sensation, touchable haptic technologies, and bio-realistic artificial intelligence systems. A flexible GaN microwire-based piezotronic sensory memory device is presented to sense and memorize the impressions of tactile information. The device and its array are capable to be programmed into a high resistance state by inputting external strain, and reversibly erased back to the low resistance state with an electrical voltage, showing an advance in bio-realistic artificial intelligence systems. • A flexible GaN microwire-based device is presented to emulate the functions of the biological sensory memory system. • The device can be programmed into HRS by external strain, and reversibly erased back to LRS with electrical voltage. • A distinct spatial mapping of external strain sensing and retaining is demonstrated in the touchable sensory memory array.

A bioinspired analogous nerve towards artificial intelligence

A bionic artificial device commonly integrates various distributed functional units to mimic the functions of biological sensory neural system, bringing intricate interconnections, complicated structure, and interference in signal transmission. Here we show an all-in-one bionic artificial nerve based on a separate electrical double-layers structure that integrates the functions of perception, recognition, and transmission. The bionic artificial nerve features flexibility, rapid response (<21 ms), high robustness, excellent durability (>10,000 tests), personalized cutability, and no energy consumption when no mechanical stimulation is being applied. The response signals are highly regionally differentiated for the mechanical stimulations, which enables the bionic artificial nerve to mimic the spatiotemporally dynamic logic of a biological neural network. Multifunctional touch interactions demonstrate the enormous potential of the bionic artificial nerve for human-machine hybrid perceptual enhancement. By incorporating the spatiotemporal resolution function and algorithmic analysis, we hope that bionic artificial nerves will promote further development of sophisticated neuroprosthetics and intelligent robotics.

Memristors Based on the Hybrid Structure of Oxide and Boron Nitride Nanosheets Combining Memristive and Neuromorphic Functionalities

Resistance random access memory (RRAM) based on a solid‐electrolyte insulator is an indispensable nanoscale device with promising potential in nonvolatile memory, multilevel switching, and neuromorphic applications. However, the random nature of the nucleation and growth of the conductive filaments (CFs) leads to instability of the switching parameter, which is the main obstacle to improving the performance of the RRAM. Herein, a novel approach to resolve this challenge by inserting boron nitride nanosheets (BNNSs) into the ZnO‐based device structure is reported. The hybrid structure with double switching layer device can combine memristive functions—nonvolatile bipolar memory, multilevel switching, and neuromorphic functionalities—simulating biological sensory systems in response to external stimuli. The overall device performance of the ZnO/BNNSs‐based memristor opens up a new path to improve the stability of oxide‐based RRAM, enabling hardware implementation of the nervous system.

Organic Synapses for Neuromorphic Electronics: From Brain-Inspired Computing to Sensorimotor Nervetronics.

Living organisms have a long evolutionary history that has provided them with functions and structures that enable them to survive in their environment. The goal of biomimetic technology is to emulate these traits of living things. Research in bioinspired electronics develops electronic sensors and motor systems that mimic biological sensory organs and motor systems and that are intended to be used in bioinspired applications such as humanoid robots, exoskeletons, and other devices that combine a living body and an electronic device. To develop bioinspired robotic and electronic devices that are compatible with the living body at the neuronal level and that are operated by mechanisms similar to those in a living body, researchers must develop biomimetic electronic sensors, motor systems, brains, and nerves. Artificial organic synapses have emulated the brain's plasticity with much simpler structures and lower fabrication cost than neurons based on silicon circuits, and with smaller energy consumption than traditional von Neumann computing methods. Organic synapses are promising components of future neuromorphic systems. In this Account, we review recent research trends of neuromorphic systems based on organic synapses, then suggest research directions. We introduce the device structures and working mechanisms of reported organic synapses and the brain's plasticity, which are mainly imitated to demonstrate the learning and memory function of the organic synapses. We also introduce recent reports on sensory synapses and sensorimotor nervetronics that mimic biological sensory and motor nervous systems. Sensory nervetronics can be used to augment the sensory functions of the living body and to comprise the sensory systems of biomimetic robots. Organic synapses can also be used to control biological muscles and artificial muscles that have the same working mechanism as biological muscle. Motor nervetronics would impart life-like motion to bioinspired robots. Chemical approaches may provide insights to guide development of new organic materials, device structures, and working mechanisms to improve synaptic responses of organic neuromorphic systems. For example, organic synapses can be applied to electronic and robotic skins and bioimplantable medical devices that use mechanically stable, self-healing, and biocompatible organic materials. Biochemical approaches may expand the plasticity of the brain and nervous system. We expect that organic neuromorphic systems will be vital components in bioinspired robotic and electronic applications, including biocompatible neural prosthetics, exoskeletons, humanoid soft robots, and cybernetics devices that are integrated with biological and artificial organs.