Biological Nervous Systems Research Articles

Analog Implementation of a Spiking System for Performing Arithmetic Logic Operations on Mixed‐Signal Neuromorphic Processors

In recent years, physical limitations in the integration of transistors in computers have forced the search for low‐computational‐power alternatives in hardware design. Although doubts may arise regarding the limit of the relationship between performance and power consumption in computers, these disappear when considering the brain, which is one of the most efficient computing systems. In this way, bioinspired applications try to benefit from the low‐power consumption present in the biological nervous system. Previous work has shown the feasibility of implementing spiking neural networks that operate in a Boolean manner on digital platforms, such as SpiNNaker, using basic logic gates and a spiking memory, which suggests the potential for constructing a low‐power consumption spiking computer. This work takes a first step in the implementation of a spiking central processing unit by developing an arithmetic logic unit, which is an essential block for instruction execution, demonstrating its correct operation on Dynap‐SE1. The results confirm the feasibility of using this Boolean approach on this platform, despite certain limitations in the number of inputs and operating frequencies of the blocks, and pave the way for the construction of a spiking computer.

Brain-Inspired Architecture for Spiking Neural Networks.

Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance. However, the biological neuron system does not perform a separate preprocessing step. Moreover, the nervous system may not have a single pathway with which to respond and process external stimuli but allows multiple circuits to perceive the same stimulus. Inspired by these advantageous aspects of the biological neural system, we propose a self-adaptive encoding spike neural network with parallel architecture. The proposed network integrates the input-encoding process into the spiking neural network architecture via convolutional operations such that the network can accept the real-valued input and automatically transform it into spikes for further processing. Meanwhile, the proposed network contains two identical parallel branches, inspired by the biological nervous system that processes information in both serial and parallel. The experimental results on multiple image classification tasks reveal that the proposed network can obtain competitive performance, suggesting the effectiveness of the proposed architecture.

An Aquatic Autonomic Nervous System.

Reproducing human nervous systems with endogenous mechanisms has attracted increasing attention, driven by its great potential in streamlining the neuro-electronic interfaces with bilateral signaling. Here, an artificial aquatic autonomic nervous system (ANS) with switchable excitatory/inhibitory characteristics and acetylcholine (ACh)-mediated plasticity is reported based on the newly emerged organic photoelectrochemical transistor (OPECT). Under the modulation of spatial light and ACh, the system exhibits an immediate switch between excitation and inhibition, and many pulse patterns as well as advanced ANS functions are mimicked. To demonstrate its potential usage, the artificial ANS is then utilized to control artificial pupils and muscles to emulate real biological responses during an emergency. In contrast to previous solid-state attempts, this ANS is aqueous compatible just like biological nervous systems, which are capable of real neurotransmitter mediation.

Proton-gated organic thin-film transistors for leaky integrate-and-fire convolutional spiking neural networks

Artificial spiking neurons, integral to the functionality of spiking neural networks, are designed to mimic the information transmission via discrete spikes in biological nervous systems. Traditional approaches that necessitate the charging of capacitors and the inclusion of discharge circuits for neuron membrane potential integration and leakage, present challenges in terms of cost and space efficiency. To overcome the challenges, this work proposes a hardware leaky integrate-and-fire neuron based on organic thin-film transistors. Under the electric field, the ion dynamics in the gate electrolyte can mimic the processes of membrane potential integration, leakage, and reset in spiking neurons. The convolutional spiking neural networks composed of such organic spiking neurons achieves excellent recognition rates (∼97.26 %) on the MNIST dataset. This indicates that the organic spiking neuron has enormous potential in next-generation non-von Neumann neuromorphic computing.

Frequency synaptic behavior of ZnO/HfZrO memristor with pulsed stimuli

The synaptic response of ZnO/HfZrO memristor devices to electrical stimuli was studied. It was found that the frequency of the stimuli affects the rate of current increase, which can be explained by the cluster state of oxygen vacancies at the interface of ZnO/HfZrO. This variation in the synaptic response due to stimulus frequency was consistent with the dynamic adjustment of tolerance thresholds observed in the human brain's response to environmental stimuli. The rate of change in current, representing the sensitivity to stimuli, mirrors the biological nervous system's reaction to environmental changes, such as Ebbinghaus forgetting behavior. Additionally, frequent irregular changes in stimuli lead to a reduced lifespan of the devices, highly resembling the biological lifetime-injure on frequent environmental fluctuations. These findings highlight the memristors' significant similarity to biological nerves in response to stimuli, confirming their tremendous potential in bionic synaptic simulation applications.

Application of Artificial Neural Network Analysis in Predicting the Performance of Microbial Energy Cells

Objective: The objective of this study was to apply Artificial Neural Networks to evaluate the performance of Microbial Energy Cells, to identify the best network configuration for cell evaluation. Theoretical Framework: Although several of the widely used effluent treatment methods show results, most of them have a common disadvantage: they lose the chemical energy contained in the treated effluent and have high energy consumption for their conduction. Therefore, an increasing effort has been made to develop effluent treatment technologies capable of recovering part of the energy contained in the waste to be treated. In this scenario, microbial energy cells (CEM) emerge as a potential technology, as they are devices that simultaneously treat effluent biologically and generate electrical energy. Methodology: For the application and evaluation of ANNs in CEM, a feedforward neural network was used, with a Levenberg-Marquardt training algorithm, 1 or 2 hidden layers, with sigmoid and tansig activation functions, and an accuracy factor of 10-5. The data used for training and validation for the ANN were obtained through a literature search. Networks with 15, 30, 50, 90, 100, 130, 150, and 200 neurons were used for testing to evaluate the best performance. Results and Discussion: With the results obtained, it was observed that the best adjustment of the network occurred with the 2-layer configuration, one layer with 100 neurons and the other output layer, with 49 interactions and R2 of 0.91 in the training adjustment, 0 .78 in the validation fit and 0.90 in the fit with all experimental data evaluated, respectively. Originality/Value: This study contributes to the literature by evaluating the application of artificial neural networks, which are empirical modeling mechanisms, inspired by biological nervous systems, with processing abilities, in microbial energy cells.

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

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.

Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks

The construction of buildings invariably involves time and costs, and disruptions impact ongoing construction projects. Crisis situations in management strategies, structural confusion, and financial miscalculations often arise due to misguided decision-making. This article proposes a method that combines the learning of Bayesian Networks and heuristic techniques to optimize decision-making processes in construction scheduling. As an innovative approach in order to enhance construction management, the functioning of biological, molecular, and physical objects and nervous systems is considered, applying bionic features to mimic their efficiency and precision, thereby optimizing construction processes and improving coordination and decision-making. Bayesian Networks are used for probabilistic analysis, and heuristic methods guide quick decision-making. The results demonstrate the effectiveness of Bayesian Networks and heuristic methods in data analysis and decision-making in construction engineering. The developed algorithm can be successfully applied to both erecting and planning construction projects.

Spike timing-based coding in neuromimetic tactile system enables dynamic object classification.

Rapid processing of tactile information is essential to human haptic exploration and dexterous object manipulation. Conventional electronic skins generate frames of tactile signals upon interaction with objects. Unfortunately, they are generally ill-suited for efficient coding of temporal information and rapid feature extraction. In this work, we report a neuromorphic tactile system that uses spike timing, especially the first-spike timing, to code dynamic tactile information about touch and grasp. This strategy enables the system to seamlessly code highly dynamic information with millisecond temporal resolution on par with the biological nervous system, yielding dynamic extraction of tactile features. Upon interaction with objects, the system rapidly classifies them in the initial phase of touch and grasp, thus paving the way to fast tactile feedback desired for neuro-robotics and neuro-prosthetics.

Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves

The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.

Reconfigurable optoelectronic transistors for multimodal recognition

Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device’s superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects.

A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector-matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software-based neural networks, the neuromorphic system unavoidably exhibits non-ideal characteristics that can have an adverse impact on overall system performance. In this study, the characteristics required for synaptic devices and their importance are discussed, depending on the targeted application. We categorize synaptic devices into two types: conductance-based and capacitance-based, and thoroughly explore the operations and characteristics of each device. The array structure according to the device structure and the VMM operation mechanism of each structure are analyzed, including recent advances in array-level implementation of synaptic devices. Furthermore, we reviewed studies to minimize the effect of hardware non-idealities, which degrades the performance of hardware neural networks. These studies introduce techniques in hardware and signal engineering, as well as software-hardware co-optimization, to address these non-idealities through compensation approaches.

Perovskite‐Nanowire‐Array‐Based Continuous‐State Programmable Artificial Synapse for Neuromorphic Computing

Perovskite‐based memristors with tunable nonvolatile states are developed to mimic the synaptic interconnects of biological nervous systems and map neuromorphic computing networks to integrated circuits. To emulate the plasticity of synaptic structures, memristors with robust multilevel resistive states are fabricated in this work using high‐density polycrystalline MAPbCl3 nanowires (NWs) array that vertically integrated using solution method. In particular, the fabricated memristors exhibit both short‐ and long‐term plasticity and traits akin to biological synapses. A fabricated memristor device is precisely programmed to 18 resistive states and each state exhibits stable data retention of more than 100 000 s. Furthermore, a matrix processing unit using a 4‐by‐4 memristor array is fabricated as the hardware core of an encoder–decoder artificial neural network to demonstrate high accuracy and reliable in‐image font conversion. The resistive states of the 16 memristors are precisely programmed to the corresponding resistance values for specific synaptic weights of the artificial‐neural‐network‐trained offline. In addition, experimental characterization and first‐principles simulations attribute the continuous programmability and high reliability features of the memristors to the confinement mechanisms of the horizontal grain‐boundary structure in polycrystalline perovskite NWs.

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.

CoDC: Accurate Learning with Noisy Labels via Disagreement and Consistency.

Inspired by the biological nervous system, deep neural networks (DNNs) are able to achieve remarkable performance in various tasks. However, they struggle to handle label noise, which can poison the memorization effects of DNNs. Co-teaching-based methods are popular in learning with noisy labels. These methods cross-train two DNNs based on the small-loss criterion and employ a strategy using either "disagreement" or "consistency" to obtain the divergence of the two networks. However, these methods are sample-inefficient for generalization in noisy scenarios. In this paper, we propose CoDC, a novel Co-teaching-basedmethod for accurate learning with label noise via both Disagreement and Consistency strategies. Specifically, CoDC maintains disagreement at the feature level and consistency at the prediction level using a balanced loss function. Additionally, a weighted cross-entropy loss is proposed based on information derived from the historical training process. Moreover, the valuable knowledge involved in "large-loss" samples is further developed and utilized by assigning pseudo-labels. Comprehensive experiments were conducted on both synthetic and real-world noise and under various noise types. CoDC achieved 72.81% accuracy on the Clothing1M dataset and 76.96% (Top1) accuracy on the WebVision1.0 dataset. These superior results demonstrate the effectiveness and robustness of learning with noisy labels.

Inference of network connectivity from temporally binned spike trains

BackgroundProcessing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored. New methodWe infer synaptic weights by processing firing rates within variable time bins for a heterogeneous feed-forward network of excitatory, inhibitory, and unconnected units. We assess classification and optimize model parameters for postsynaptic spike train reconstruction. We test our method on a physiological network of leaky integrate-and-fire neurons displaying bursting patterns and assess prediction of postsynaptic activity from microelectrode array data. ResultsResults reveal parameters for improved prediction and performance and suggest that lower resolution data and limited access to neurons can be preferred. Comparison with existing method(s)Recent computational methods demonstrate highly improved reconstruction of connectivity from networks of parallel spike trains by considering spike lag, time-varying firing rates, and other underlying dynamics. However, these methods insufficiently explore temporal subsampling representative of novel data types. ConclusionsWe provide a framework for reverse engineering neural networks from data with limited temporal quality, describing optimal parameters for each bin size, which can be further improved using non-linear methods and applied to more complicated readouts and connectivity distributions in multiple brain circuits.

DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor

With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically. The electronic systems used in these applications must process data continuously, in real-time, and extract relevant information using the smallest possible energy budgets. A promising approach for implementing always-on processing of sensory signals that supports on-demand, sparse, and edge-computing is to take inspiration from biological nervous system. Following this approach, we present a brain-inspired platform for prototyping real-time event-based spiking neural networks. The system proposed supports the direct emulation of dynamic and realistic neural processing phenomena such as short-term plasticity, NMDA gating, AMPA diffusion, homeostasis, spike frequency adaptation, conductance-based dendritic compartments and spike transmission delays. The analog circuits that implement such primitives are paired with a low latency asynchronous digital circuits for routing and mapping events. This asynchronous infrastructure enables the definition of different network architectures, and provides direct event-based interfaces to convert and encode data from event-based and continuous-signal sensors. Here we describe the overall system architecture, we characterize the mixed signal analog-digital circuits that emulate neural dynamics, demonstrate their features with experimental measurements, and present a low- and high-level software ecosystem that can be used for configuring the system. The flexibility to emulate different biologically plausible neural networks, and the chip’s ability to monitor both population and single neuron signals in real-time, allow to develop and validate complex models of neural processing for both basic research and edge-computing applications.

Brain-inspired chaotic spiking backpropagation.

Spiking neural networks (SNNs) have superior energy efficiency due to their spiking signal transmission, which mimics biological nervous systems, but they are difficult to train effectively. Although surrogate gradient-based methods offer a workable solution, trained SNNs frequently fall into local minima because they are still primarily based on gradient dynamics. Inspired by the chaotic dynamics in animal brain learning, we propose a chaotic spiking backpropagation (CSBP) method that introduces a loss function to generate brain-like chaotic dynamics and further takes advantage of the ergodic and pseudo-random nature to make SNN learning effective and robust. From a computational viewpoint, we found that CSBP significantly outperforms current state-of-the-art methods on both neuromorphic data sets (e.g. DVS-CIFAR10 and DVS-Gesture) and large-scale static data sets (e.g. CIFAR100 and ImageNet) in terms of accuracy and robustness. From a theoretical viewpoint, we show that the learning process of CSBP is initially chaotic, then subject to various bifurcations and eventually converges to gradient dynamics, consistently with the observation of animal brain activity. Our work provides a superior core tool for direct SNN training and offers new insights into understanding the learning process of a biological brain.

Gate-Controlled Neuromorphic Functional Transition in an Electrochemical Graphene Transistor.

Neuromorphic devices have attracted significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential components in artificial neural networks such as synapses and neurons are predominantly implemented by dedicated devices with specific functionalities. In this work, we present a gate-controlled transition of neuromorphic functions between artificial neurons and synapses in monolayer graphene transistors that can be employed as memtransistors or synaptic transistors as required. By harnessing the reliability of reversible electrochemical reactions between carbon atoms and hydrogen ions, we can effectively manipulate the electric conductivity of graphene transistors, resulting in a high on/off resistance ratio, a well-defined set/reset voltage, and a prolonged retention time. Overall, the on-demand switching of neuromorphic functions in a single graphene transistor provides a promising opportunity for developing adaptive neural networks for the upcoming era of artificial intelligence and machine learning.