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The editorial in Frontiers in Computational Neuroscience discusses the complex nature of cognition and perception in the brain, highlighting the role of computational models in understanding neural processes. Various studies presented in the research topic focus on aspects such as memory capacity, information coding, and neural activity in response to external stimuli. The research aims to provide insights into the neural correlates of cognition and perception, with potential applications in fields like medicine and engineering. The work collected in this research topic serves as a foundation for future studies exploring these topics and their practical implications. [Extracted from the article]

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model's behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners. [ABSTRACT FROM AUTHOR]

This work introduces a neuromorphic sensor (NS) based on force-sensing resistors (FSR) and spiking neurons for robotic systems. The proposed sensor integrates the FSR in the schematic of the spiking neuron in order to make the sensor generate spikes with a frequency that depends on the applied force. The performance of the proposed sensor is evaluated in the control of a SMA-actuated robotic finger by monitoring the force during a steady state when the finger pushes on a tweezer. For comparison purposes, we performed a similar evaluation when the SNN received input from a widely used compression load cell (CLC). The results show that the proposed FSR-based neuromorphic sensor has very good sensitivity to low forces and the function between the spiking rate and the applied force is continuous, with good variation range. However, when compared to the CLC, the response of the NS follows a logarithmic-like function with improved sensitivity for small forces. In addition, the power consumption of NS is 128 µW that is 270 times lower than that of the CLC which needs 3.5 mW to operate. These characteristics make the neuromorphic sensor with FSR suitable for bioinspired control of humanoid robotics, representing a low-power and low-cost alternative to the widely used sensors. [ABSTRACT FROM AUTHOR]

Gamma oscillations are widely seen in the cerebral cortex in different states of the wake-sleep cycle and are thought to play a role in sensory processing and cognition. Here, we study the emergence of gamma oscillations at two levels, in networks of spiking neurons, and a mean-field model. At the network level, we consider two different mechanisms to generate gamma oscillations and show that they are best seen if one takes into account the synaptic delay between neurons. At the mean-field level, we show that, by introducing delays, the mean-field can also produce gamma oscillations. The mean-field matches the mean activity of excitatory and inhibitory populations of the spiking network, as well as their oscillation frequencies, for both mechanisms. This mean-field model of gamma oscillations should be a useful tool to investigate large-scale interactions through gamma oscillations in the brain. [ABSTRACT FROM AUTHOR]

To tackle environmental and increasing energy demand issues, different energy transition options have been investigated. Solar power has vast resources and is environment-friendly, making it a possible alternative to fossil fuels. However, its integration into the power system poses many challenges because of its uncertain variability, and different deep-learning techniques have been put forward to address its intermittent nature. However, these techniques pose challenges related to high computational overhead and power requirements. Therefore, we present a deep-learning technique leveraging the Leaky Integrated and Fire (LIF) spiking neurons for short-term forecasting of Global Horizontal Irradiance (GHI). The proposed NeuroSpike network consists of a Recurrent Neural Network (RNN) layer, initialized with LIF spiking neurons and stacked with the conventional Long Short-Term Memory (LSTM) layer. The historical GHI data from three distinct locations in the Kingdom of Saudi Arabia (KSA) is used in this study. In the data preprocessing step, a Recursive Feature Elimination with Categorical Boosting (RFE-CatBoost) algorithm is used to select the appropriate features that inherently describe the dataset patterns. The proposed NeuroSpike network is trained on the selected features, and its forecast performance is compared with different benchmark techniques reported in the literature. The results demonstrate that the NeuroSpike network has lower forecasting errors than the techniques compared. Moreover, with RFE-CatBoost algorithm-based feature selection, an improvement of 30.33%, 43.12%, and 23.4% is recorded in the Mean Absolute Error (MAE) of the NeuroSpike network for the datasets of Al-Jouf, Qassim and K.A.CARE sites, respectively. The findings illustrate that the NeuroSpike network’s training becomes more effective and computationally less demanding due to the integration of spiking neurons and the proposed RFE-CatBoost feature selection technique.

Implementing and integrating spiking neurons for neuromorphic hardware realization conforming to spiking neural networks holds great promise in enabling efficient learning and decision‐making. The spiking neurons, however, may lack the spiking dynamics to encode the dynamical information in complex real‐world problems. Herein, using filamentary memristors from solution‐processed hexagonal boron nitride, this study assembles leaky integrate‐and‐fire spiking neurons and, particularly, harnesses the common switching stochasticity feature in the memristors to allow key neural dynamics, including Poisson‐like spiking and adaptation. The neurons, with the dynamics fitted via hardware‐algorithm codesign, suggest a potential in realizing spike‐based neuromorphic hardware capable of handling complex problems. Simulation of an autoencoder for anomaly detection of time‐series real analog and digital data from physical systems is demonstrated, underscoring its promising prospect in applications, especially, at the edges with limited computation resources, for instance, auto‐pilot, manufacturing, wearables, and Internet of things. [ABSTRACT FROM AUTHOR]

Supervised learning of spiking neurons is an effective simulation method to explore the learning mechanism of real neurons. Desired output spike trains are often used as supervised signals to control the synaptic strength adjustment of neurons for precise emission. The goal of supervised learning is also to allow spiking neurons to enter the desired running and firing state. The running process of a spiking neuron is a continuous process, but because of absolute refractory periods, it is regarded as several running segments. Based on the segmental characteristic, a new supervised learning strategy for spiking neurons is proposed to expand the action mode of supervised signals in supervised learning. Desired output spikes are used to actively regulate the running segments and make them more efficient in achieving the desired running and firing state. Supervised signals actively regulate the running process of neurons and are more comprehensively involved in the learning process than simply participating in adjusting synaptic weights. Based on two weight adjustment mechanisms of spiking neurons, two new specific supervised learning methods are proposed. The experimental results obtained using various settings indicate that the two learning methods have higher learning performance, which indicates the effectiveness of the new learning strategy. [ABSTRACT FROM AUTHOR]

Memristive devices with threshold switching characteristics can be effectively utilized to mimic biological neurons acting as one of the key building blocks for constructing advanced hardware neural networks. In this work, the emulation of leaky integrate‐and‐fire memristive neuron is realized in one single cell with Ag/Ag−In−Zn−S/silk sericin/W architecture without the need for additional auxiliary circuits. The studied devices demonstrate excellent electrical properties, such as stably repeatable threshold switching, concentratedly low threshold voltage (≈0.4 V), and relatively small device‐to‐device variation. In addition, multiple neural features, such as leaky integrate‐and‐fire neuron functionality and strength‐modulated spike frequency characteristic, have been successfully emulated owing to the forming‐free volatile threshold switching effect. The stable volatile threshold switching behaviors and regular firing event may be attributed to the controllable metallic Ag filamentary mechanism. Furthermore, a solid accuracy of 91.44% of the pattern recognition of Modified National Institute of Standards and Technology (MNIST) data is obtained via a trained spiking neural network (SNN) based on the leaky integrate‐and‐fire behavior of sericin‐based device. These achievements shed light on the fact that employing sericin biomaterials has great application potential in advanced neuromorphic computation. [ABSTRACT FROM AUTHOR]

Abstract Implementing and integrating spiking neurons for neuromorphic hardware realization conforming to spiking neural networks holds great promise in enabling efficient learning and decision‐making. The spiking neurons, however, may lack the spiking dynamics to encode the dynamical information in complex real‐world problems. Herein, using filamentary memristors from solution‐processed hexagonal boron nitride, this study assembles leaky integrate‐and‐fire spiking neurons and, particularly, harnesses the common switching stochasticity feature in the memristors to allow key neural dynamics, including Poisson‐like spiking and adaptation. The neurons, with the dynamics fitted via hardware‐algorithm codesign, suggest a potential in realizing spike‐based neuromorphic hardware capable of handling complex problems. Simulation of an autoencoder for anomaly detection of time‐series real analog and digital data from physical systems is demonstrated, underscoring its promising prospect in applications, especially, at the edges with limited computation resources, for instance, auto‐pilot, manufacturing, wearables, and Internet of things.

Abstract Memristive devices with threshold switching characteristics can be effectively utilized to mimic biological neurons acting as one of the key building blocks for constructing advanced hardware neural networks. In this work, the emulation of leaky integrate‐and‐fire memristive neuron is realized in one single cell with Ag/Ag−In−Zn−S/silk sericin/W architecture without the need for additional auxiliary circuits. The studied devices demonstrate excellent electrical properties, such as stably repeatable threshold switching, concentratedly low threshold voltage (≈0.4 V), and relatively small device‐to‐device variation. In addition, multiple neural features, such as leaky integrate‐and‐fire neuron functionality and strength‐modulated spike frequency characteristic, have been successfully emulated owing to the forming‐free volatile threshold switching effect. The stable volatile threshold switching behaviors and regular firing event may be attributed to the controllable metallic Ag filamentary mechanism. Furthermore, a solid accuracy of 91.44% of the pattern recognition of Modified National Institute of Standards and Technology (MNIST) data is obtained via a trained spiking neural network (SNN) based on the leaky integrate‐and‐fire behavior of sericin‐based device. These achievements shed light on the fact that employing sericin biomaterials has great application potential in advanced neuromorphic computation.

A method is proposed by which the power consumption of a biologically detailed digital neuron implementation can be reduced without modification to the digital neuron's hardware architecture and independent of the neuron model. This method results in substantial power savings by causing the neuron to enter a quasi-functional state when low input stimulus is received. This approach is analogous to the function of real biological neurons as they enter a low-activity state for low stimulus. The shifts in neuronal activity created by the novel method allow for the membrane potential to remain uncorrupted over a large domain of input synaptic current, while avoiding unnecessary computations and switching activity. The digital hardware implementation results are presented and discussed, and it is shown that the behaviour of the neuron is unaffected using the novel method. The power consumption of the implemented digital neurons is compared with traditional implementations, and considerable power savings are shown. [ABSTRACT FROM AUTHOR]

Classification and recognition tasks performed on photonic hardware-based neural networks often require at least one offline computational step, such as in the increasingly popular reservoir computing paradigm. Removing this offline step can significantly improve the response time and energy efficiency of such systems. We present numerical simulations of different algorithms that utilize ultrafast photonic spiking neurons as receptive fields to allow for image recognition without an offline computing step. In particular, we discuss the merits of event, spike-time and rank-order based algorithms adapted to this system. These techniques have the potential to significantly improve the efficiency and effectiveness of optical classification systems, minimizing the number of spiking nodes required for a given task and leveraging the parallelism offered by photonic hardware.

This paper introduces a new approach to neuromorphic photonics in which microcavities exhibiting strong exciton–photon interaction may serve as building blocks of optical spiking neurons. The experimental results demonstrate the intrinsic property of exciton–polaritons to resemble the Leaky Integrate‐and‐Fire (LIF) spiking mechanism. It is shown that exciton–polariton microcavities when non‐resonantly pumped with a pulsed laser exhibit leaky integration due to relaxation of the excitonic reservoir, threshold‐and‐fire mechanism due to transition to Bose–Einstein Condensate (BEC), and resetting due to stimulated emission of photons. These effects, evidenced in photoluminescence characteristics, arise within sub‐ns timescales. The presented approach provides means for ultrafast processing of spike‐like laser pulses with energy efficiency at the level below 1 pJ per spike. [ABSTRACT FROM AUTHOR]

A thorny problem in machine learning is how to extract useful clues related to delayed feedback signals from the clutter of input activity, known as the temporal credit-assignment problem. The aggregate-label learning algorithms make an explicit representation of this problem by training spiking neurons to assign the aggregate feedback signal to potentially effective clues. However, earlier aggregate-label learning algorithms suffered from ineficiencies due to the large amount of computation, while recent algorithms that have solved this problem may fail to learn due to the inability to find adjustment points. Therefore, we propose a membrane voltage slope guided algorithm (VSG) to further cope with this limitation. Direct dependence on the membrane voltage when finding the key point of weight adjustment makes VSG avoid intensive calculation, butmore importantly, themembrane voltage that always exists makes it impossible to lose the adjustment point. Experimental results show that the proposed algorithm can correlate delayed feedback signals with the effective clues embedded in background spiking activity, and also achieves excellent performance on real medical classification datasets and speech classification datasets. The superior performancemakes it ameaningful reference for aggregate-label learning on spiking neural networks. [ABSTRACT FROM AUTHOR]

A highly accurate digital implementation of the Morris-Lecar neuron model is presented with the intended application of hardware acceleration for neuroscience simulation. The novel implementation employs the COordinate Rotation DIgital Computer (CORDIC) algorithm to create a fixed-point implementation that is not only very accurate but requires low digital hardware resources. The accuracy exceeds that of the current state-of-the-art, requires fewer hardware resources to implement, and operates at a higher maximum clock frequency. The design is validated on FPGA and a normalized RMSE of 0.2039 is achieved at a maximum clock frequency of 378.07MHz. [ABSTRACT FROM AUTHOR]

We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intelligence (AI) algorithms for mechanics applications. With recent developments like the introduction of neural operators, AI's potential for being used in mechanics applications has increased significantly. However, AI's immense energy and resource requirements are a hurdle in its practical field use case. The proposed VS-WNO is based on the principles of spiking neural networks, which have shown promise in reducing the energy requirements of the neural networks. This makes possible the use of such algorithms in edge computing. The proposed VS-WNO utilizes variable spiking neurons, which promote sparse communication, thus conserving energy, and its use is further supported by its ability to tackle regression tasks, often faced in the field of mechanics. Various examples dealing with partial differential equations, like Burger's equation, Allen Cahn's equation, and Darcy's equation, have been shown. Comparisons have been shown against wavelet neural operator utilizing leaky integrate and fire neurons (direct and encoded inputs) and vanilla wavelet neural operator utilizing artificial neurons. The results produced illustrate the ability of the proposed VS-WNO to converge to ground truth while promoting sparse communication. • Neuroscience inspired operator learning is proposed for scientific computing. • Proposed VS-WNO promotes sparse communications and is energy efficient. • We introduce a tailored spiking loss function to limit spiking activity. • Numerical examples solved illustrate accuracy and energy efficiency of VS-WNO. [ABSTRACT FROM AUTHOR]

The human brain constantly learns and rapidly adapts to new situations by integrating acquired knowledge and experiences into memory. Developing this capability in machine learning models is considered an important goal of AI research since deep neural networks perform poorly when there is limited data or when they need to adapt quickly to new unseen tasks. Meta-learning models are proposed to facilitate quick learning in low-data regimes by employing absorbed information from the past. Although some models have recently been introduced that reached high-performance levels, they are not biologically plausible. In our research, we have proposed a bio-plausible meta-learning model inspired by the hippocampus and the prefrontal cortex using spiking neural networks with a reward-based learning system. The major contribution of our work lies in the design of a bio-plausible meta-learning framework that incorporates learning rules such as Spike-Timing-Dependent Plasticity (STDP) and Reward-Modulated STDP (R-STDP). This framework not only reflects biological learning mechanisms more accurately but also attains competitive results comparable to those achieved by traditional gradient descent-based approaches in meta-learning. Our proposed model includes a memory designed to prevent catastrophic forgetting, a phenomenon that occurs when meta-learning models forget what they have learned so far as learning the new task begins. Furthermore, our new model can easily be applied to spike-based neuromorphic devices and enables fast learning in neuromorphic hardware. The implications and predictions of various models for solving few-shot classification tasks are extensively analyzed. Base on the results, our model has demonstrated the ability to compete with the existing state-of-the-art meta-learning techniques, representing a significant step towards creating AI systems that emulate the human brain's ability to learn quickly and efficiently from limited data. • "Higher accuracy & generalization w.r.t SOTA methods in few-shot classification tasks." • "Improved the generalization of meta-SNNs by simulating an efficient episodic memory." • "Demonstrating the potential of using reward-modulated STDP in SNNS for meta-learning." [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are the basis of recent advances in artificial intelligence (AI); they typically use real valued neuron responses. By contrast, biological neurons are known to operate using spike trains. In principle, spiking neural networks (SNNs) may have a greater representational capability than ANNs, especially for time series such as speech; however their adoption has been held back by both a lack of stable training algorithms and a lack of compatible baselines. We begin with a fairly thorough review of literature around the conjunction of ANNs and SNNs. Focusing on surrogate gradient approaches, we proceed to define a simple but relevant evaluation based on recent speech command tasks. After evaluating a representative selection of architectures, we show that a combination of adaptation, recurrence and surrogate gradients can yield light spiking architectures that are not only able to compete with ANN solutions, but also retain a high degree of compatibility with them in modern deep learning frameworks. We conclude tangibly that SNNs are appropriate for future research in AI, in particular for speech processing applications, and more speculatively that they may also assist in inference about biological function. [ABSTRACT FROM AUTHOR]

Recent studies have shown that alpha oscillations (8–13 Hz) enable the decoding of auditory spatial attention. Inspired by sparse coding in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The proposed model can extract the patterns of recorded EEG of leftward and rightward attention, independently, and uses them to train the network to detect auditory spatial attention. Specifically, our model is composed of three layers, two of which are Integrate and Fire spiking neurons. We formulate a new learning rule that is based on the firing rate of pre- and post-synaptic neurons in the first and second layers of spiking neurons. The third layer has 10 spiking neurons and the pattern of their firing rate is used in the test phase to decode the auditory spatial attention of a given test sample. Moreover, the effects of using low connectivity rates of the layers and specific range of learning parameters of the learning rule are investigated. The proposed model achieves an average accuracy of 90% with only 10% of EEG signals as training data. This study also provides new insights into the role of sparse coding in both cortical networks subserving cognitive tasks and brain-inspired machine learning. [ABSTRACT FROM AUTHOR]

The human receives and transmits various information from the outside world through different sensory systems. The sensory neurons integrate various sensory inputs into a synthetical perception to monitor complex environments, and this fundamentally determines the way how we perceive the world. Developing multifunctional artificial sensory elements that can integrate multisensory perception plays a vital role in future intelligent perception systems, whereas prior spiking neurons reported can only handle single‐mode physical signals. Herein, a bioinspired haptic‐temperature fusion spiking neuron based upon a serial connection of piezoresistive sensor and VO2 volatile memristor is presented. The artificial sensory neuron is capable of detecting and encoding pressure and temperature inputs based on the voltage dividing effect and the intrinsic thermal sensitivity of metal–insulator transition in VO2. Recognition of Braille characters is achieved through multiple piezoresistive sensors, taking advantage of the spatial integration capabilities of such spiking neurons. Notably, the traditionally separate haptic and temperature signals can be fused physically in the sensory neuron when synchronizing the two sensory cues, which is able to recognize multimodal haptic/temperature patterns. The artificial multisensory neuron thus provides a promising approach toward e‐skin, neurorobotics, and human–machine interaction technologies. A preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.164668806.60849882. [ABSTRACT FROM AUTHOR]

Since spiking neural networks (SNNs) can effectively simulate the information processing mechanism of the biological cortex, they are expected to bridge the gap between neuroscience and machine learning. The hardware simulation of large-scale SNNs requires a simple and versatile silicon neuron model framework. In this article, a spiking neuron circuit as the core device of SNNs is presented. The proposed neuron circuit can mimic the dynamics of different types of biological neurons by adjusting the bias voltage. In order to facilitate the implementation of the spiking neuron circuit based on complementary metal-oxide-semiconductor (CMOS) and reduce the overhead of the circuit area, a modified Mihalas–Niebur (MN) mathematical model is adopted. The improved MN model is biologically plausible and can still successfully display all dynamic behaviors observed in biology. The function of the proposed neuron circuit has been verified by the phase diagram analysis method. The simulation results show the designed neuron circuit can successfully replicate 15 of the 20 firing patterns exhibited by the biological cortex, which proves that the neuron can act as a universal spiking neuron in very large-scale integrated circuit (VLSI) neuromorphic networks. [ABSTRACT FROM AUTHOR]

The human receives and transmits various information from the outside world through different sensory systems. The sensory neurons integrate various sensory inputs into a synthetical perception to monitor complex environments, and this fundamentally determines the way how we perceive the world. Developing multifunctional artificial sensory elements that can integrate multisensory perception plays a vital role in future intelligent perception systems, whereas prior spiking neurons reported can only handle single‐mode physical signals. Herein, a bioinspired haptic‐temperature fusion spiking neuron based upon a serial connection of piezoresistive sensor and VO2 volatile memristor is presented. The artificial sensory neuron is capable of detecting and encoding pressure and temperature inputs based on the voltage dividing effect and the intrinsic thermal sensitivity of metal–insulator transition in VO2. Recognition of Braille characters is achieved through multiple piezoresistive sensors, taking advantage of the spatial integration capabilities of such spiking neurons. Notably, the traditionally separate haptic and temperature signals can be fused physically in the sensory neuron when synchronizing the two sensory cues, which is able to recognize multimodal haptic/temperature patterns. The artificial multisensory neuron thus provides a promising approach toward e‐skin, neurorobotics, and human–machine interaction technologies. A preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.164668806.60849882.

Artificial neural networks (ANNs) are the basis of recent advances in artificial intelligence (AI); they typically use real valued neuron responses. By contrast, biological neurons are known to operate using spike trains. In principle, spiking neural networks (SNNs) may have a greater representational capability than ANNs, especially for time series such as speech; however their adoption has been held back by both a lack of stable training algorithms and a lack of compatible baselines. We begin with a fairly thorough review of literature around the conjunction of ANNs and SNNs. Focusing on surrogate gradient approaches, we proceed to define a simple but relevant evaluation based on recent speech command tasks. After evaluating a representative selection of architectures, we show that a combination of adaptation, recurrence and surrogate gradients can yield light spiking architectures that are not only able to compete with ANN solutions, but also retain a high degree of compatibility with them in modern deep learning frameworks. We conclude tangibly that SNNs are appropriate for future research in AI, in particular for speech processing applications, and more speculatively that they may also assist in inference about biological function.

Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence learning and prediction. This form of computation is essential for almost all our daily tasks such as movement generation, perception, and language. Understanding how the brain performs such a computation is not only important to advance neuroscience, but also to pave the way to new technological brain-inspired applications. A previously developed spiking neural network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. An emerging type of hardware that may efficiently run this type of algorithm is neuromorphic hardware. It emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential synaptic elements in neuromorphic hardware. In particular, redox-induced resistive random access memories (ReRAM) devices stand out at many aspects. They permit scalability, are energy efficient and fast, and can implement biological plasticity rules. In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model. We implement and simulate the model including the ReRAM plasticity using the neural network simulator NEST. We investigate two types of ReRAM memristive devices: (i) a gradual, analog switching device, and (ii) an abrupt, binary switching device. We study the effect of different device properties on the performance characteristics of the sequence learning model, and demonstrate that, in contrast to many other artificial neural networks, this architecture is resilient with respect to changes in the on-off ratio and the conductance resolution, device variability, and device failure.

The environmental temperature plays a critical role in the system functioning. In biological organisms, there often exists an optimal temperature for the most effective functions. In this work, we investigate the effect of temperature on the propagation of firing rate in a feed-forward multilayer neural network in which neurons in the first layer are stimulated by stochastic noises. We then show that the firing rate can be transmitted through the network within a temperature range. We also show that the propagation of the firing rate by synchronization is optimized at an appropriate temperature. Our findings provide new insights and improve our understanding of the optimal temperature observed in the experiments in the living biological systems. • Effect of temperature on the propagation of firing rate is investigated. • The firing rate can be transmitted within a comfortable temperature range. • An appropriate temperature effectively improves the transmission of synchronous firings. • The temperature-optimized propagation of synchronous firing rate is universal. [ABSTRACT FROM AUTHOR]

Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method and, as such, does not require feedback, making it suitable in contexts where systems have to learn autonomously. This paper explores how molecular systems can be designed to show such protointelligent behaviors and proposes the first chemical reaction network (CRN) that can exhibit autonomous Hebbian learning across arbitrarily many input channels. The system emulates a spiking neuron, and we demonstrate that it can learn statistical biases of incoming inputs. The basic CRN is a minimal, thermodynamically plausible set of microreversible chemical equations that can be analyzed with respect to their energy requirements. However, to explore how such chemical systems might be engineered de novo, we also propose an extended version based on enzyme-driven compartmentalized reactions. Finally, we show how a purely DNA system, built upon the paradigm of DNA strand displacement, can realize neuronal dynamics. Our analysis provides a compelling blueprint for exploring autonomous learning in biological settings, bringing us closer to realizing real synthetic biological intelligence.

Multimode-fused sensing in the somatosensory system helps people obtain comprehensive object properties and make accurate judgments. However, building such multisensory systems with conventional metal-oxide-semiconductor technology presents serious device integration and circuit complexity challenges. Here, a multimode-fused spiking neuron (MFSN) with a compact structure to achieve human-like multisensory perception is reported. The MFSN heterogeneously integrates a pressure sensor to process pressure and a NbO x -based memristor to sense temperature. Using this MFSN, multisensory analog information can be fused into one spike train, showing excellent data compression and conversion capabilities. Moreover, both pressure and temperature information are distinguished from fused spikes by decoupling the output frequencies and amplitudes, supporting multimodal tactile perception. Then, a 3 × 3 MFSN array is fabricated, and the fused frequency patterns are fed into a spiking neural network for enhanced tactile pattern recognition. Finally, a larger MFSN array is simulated for classifying objects with different shapes, temperatures, and weights, validating the feasibility of the MFSNs for practical applications. The proof-of-concept MFSNs enable the building of multimodal sensory systems and contribute to the development of highly intelligent robotics., (© 2022 Wiley-VCH GmbH.)