Spiking Neural System Research Articles

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems.

Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model based on convolutional nonlinear spiking neural P systems. NSNPRIS features NSNPFusion and Language Gate modules to enhance feature interaction during encoding, along with an NSNPDecoder for feature alignment and decoding. Experimental results on RefCOCO, RefCOCO[Formula: see text], and G-Ref datasets demonstrate that NSNPRIS performs better than mainstream methods. Our contributions include advances in the alignment of pixel and textual features and the improvement of segmentation accuracy.

Asynchronous Numerical Spiking Neural Membrane Systems with Local Synchronization.

Since the spiking neural P system (SN P system) was proposed in 2006, it has become a research hotspot in the field of membrane computing. The SN P system performs computations through the encoding, processing, and transmission of spiking information and can be regarded as a third-generation neural network. As a variant of the SN P system, the global asynchronous numerical spiking neural P system (ANSN P system) is adaptable to a broader range of application scenarios. However, in biological neuroscience, some neurons work synchronously within a community to perform specific functions in the brain. Inspired by this, our work investigates a global asynchronous spiking neural P system (ANSN P system) that incorporates certain local synchronous neuron sets. Within these local synchronous sets, neurons must execute their production functions simultaneously, thereby reducing dependence on thresholds and enhancing control uncertainty in ANSN P systems. By analyzing the ADD, SUB, and FIN modules in the generating mode, as well as the INPUT and ADD modules in the accepting mode, this paper demonstrates the novel system's computational capacity as both a generator and an acceptor. Additionally, this paper compares each module to those in other SN P systems, considering the maximum number of neurons and rules per neuron. The results show that this new ANSN P system is at least as effective as the existing SN P systems.

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.

A detection method based on nonlinear spiking neural systems for infrared small targets

Due to the lack of specific shapes and textures and susceptibility to background clutter and noise interference, infrared small target detection has always been a challenging task. The nonlinear spiking neural P (NSNP) system is a type of neural computing model inspired by the nonlinear spiking mechanisms of biological neurons. It can better identify the internal relationships between small objects, and based on this. This paper presents a variant of NSNP systems, called nonlinear spiking neural P systems with two states or NSNP-TS systems. Based on this variant, a new method for infrared small target detection is proposed. The core idea is the employment of the nonlinear spiking mechanism of the NSNP-TS system. Moreover, a preprocessing component is introduced to find potential targets from the original infrared image, with the aim of making the firing mechanism of the NSNP-TS system more effective. The experimental results on real sequences from 8 different environmental backgrounds indicate that the proposed method outperforms other comparative methods.

Hypergraph-Based Numerical Spiking Neural Membrane Systems with Novel Repartition Protocols.

The classic spiking neural P (SN P) systems abstract the real biological neural network into a simple structure based on graphs, where neurons can only communicate on the plane. This study proposes the hypergraph-based numerical spiking neural membrane (HNSNM) systems with novel repartition protocols. Through the introduction of hypergraphs, the HNSNM systems can characterize the high-order relationships among neurons and extend the traditional neuron structure to high-dimensional nonlinear spaces. The HNSNM systems also abstract two biological mechanisms of synapse creation and pruning, and use plasticity rules with repartition protocols to achieve planar, hierarchical and spatial communications among neurons in hypergraph neuron structures. Through imitating register machines, the Turing universality of the HNSNM systems is proved by using them as number generating and accepting devices. A universal HNSNM system consisting of 41 neurons is constructed to compute arbitrary functions. By solving NP-complete problems using the subset sum problem as an example, the computational efficiency and effectiveness of HNSNM systems are verified.

Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay.

Spiking neural membrane systems (or spiking neural P systems, SNP systems) are a new type of computation model which have attracted the attention of plentiful scholars for parallelism, time encoding, interpretability and extensibility. The original SNP systems only consider the time delay caused by the execution of rules within neurons, but not caused by the transmission of spikes via synapses between neurons and its adaptive adjustment. In view of the importance of time delay for SNP systems, which are a time encoding computation model, this study proposes SNP systems with adaptive synaptic time delay (ADSNP systems) based on the dynamic regulation mechanism of synaptic transmission delay in neural systems. In ADSNP systems, besides neurons, astrocytes that can generate adenosine triphosphate (ATP) are introduced. After receiving spikes, astrocytes convert spikes into ATP and send ATP to the synapses controlled by them to change the synaptic time delays. The Turing universality of ADSNP systems in number generating and accepting modes is proved. In addition, a small universal ADSNP system using 93 neurons and astrocytes is given. The superiority of the ADSNP system is demonstrated by comparison with the six variants. Finally, an ADSNP system is constructed for credit card fraud detection, which verifies the feasibility of the ADSNP system for solving real-world problems. By considering the adaptive synaptic delay, ADSNP systems better restore the process of information transmission in biological neural networks, and enhance the adaptability of SNP systems, making the control of time more accurate.

An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation.

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved.

Multiple-in-Single-Out Object Detector Leveraging Spiking Neural Membrane Systems and Multiple Transformers.

Most existing multi-scale object detectors depend on multi-level feature maps. The Feature Pyramid Networks (FPN) is a significant architecture for object detection that utilizes these multi-level feature maps. However, the use of FPN also increases the detector's complexity. For object detection methods that only use a single-level feature map, the detection performance is limited to some extent because the single-level feature map cannot balance deep semantic information and shallow detail information. We introduce a novel detector- the Spiking Neural P Multiple-in-Single-out (SNPMiSo) detector to address these challenges. The SNPMiSo detector is constructed based on SNP-like neurons. In SNPMiSo, we employ two kinds of Transformers to boost the important features across different-level feature maps separately. After enhancing the features, we use an incremental upsampling module to upsample and merge the two feature maps. This combined feature map is input into the NAF dilated residual module and the NAF dual-branch detection head. This process allows us to extract multi-scale features and carry out detection tasks. Our tests show promising results: On the COCO dataset, SNPMiSo attains an Average Precision (AP) of 38.7, an improvement of 1.0 AP over YOLOF.In addition, SNPMiSo demonstrates a quicker detection speed, outperforming some advanced multi-level and single-level object detectors.

An Asynchronous Spiking Neural Membrane System for Edge Detection.

Spiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the enabled neurons in the same group connected by resonant connections should instantly produce spikes with the same rhythm. In the designed system, each of the three modules implements one type of the three operations associated with the edge detection of digital images, and they collaborate each other through the resonant connections. An algorithm called EDSNP for edge detection is proposed to simulate the working of the designed asynchronous SN P system. A quantitative analysis of EDSNP and the related methods for edge detection had been conducted to evaluate the performance of EDSNP. The performance of the EDSNP in processing the testing images is superior to the compared methods, based on the quantitative metrics of accuracy, error rate, mean square error, peak signal-to-noise ratio and true positive rate. The results indicate the potential of the temporal firing and the proper neuronal connections in the SN P system to achieve good performance in edge detection.

A Parallel Convolutional Network Based on Spiking Neural Systems.

Deep convolutional neural networks have shown advanced performance in accurately segmenting images. In this paper, an SNP-like convolutional neuron structure is introduced, abstracted from the nonlinear mechanism in nonlinear spiking neural P (NSNP) systems. Then, a U-shaped convolutional neural network named SNP-like parallel-convolutional network, or SPC-Net, is constructed for segmentation tasks. The dual-convolution concatenate (DCC) and dual-convolution addition (DCA) network blocks are designed, respectively, in the encoder and decoder stages. The two blocks employ parallel convolution with different kernel sizes to improve feature representation ability and make full use of spatial detail information. Meanwhile, different feature fusion strategies are used to fuse their features to achieve feature complementarity and augmentation. Furthermore, a dual-scale pooling (DSP) module in the bottleneck is designed to improve the feature extraction capability, which can extract multi-scale contextual information and reduce information loss while extracting salient features. The SPC-Net is applied in medical image segmentation tasks and is compared with several recent segmentation methods on the GlaS and CRAG datasets. The proposed SPC-Net achieves 90.77% DICE coefficient, 83.76% IoU score and 83.93% F1 score, 86.33% ObjDice coefficient, 135.60 Obj-Hausdorff distance, respectively. The experimental results show that the proposed model can achieve good segmentation performance.

Modular Spiking Neural Membrane Systems for Image Classification.

A variant of membrane computing models called Spiking Neural P systems (SNP systems) closely mimics the structure and behavior of biological neurons. As third-generation neural networks, SNP systems have flexible architectures allowing the design of bio-inspired machine learning algorithms. This paper proposes Modular Spiking Neural P (MSNP) systems to solve image classification problems, a novel SNP system to be applied in scenarios where hundreds or even thousands of different classes are considered. A main issue to face in such situations is related to the structural complexity of the network. MSNP systems devised in this work allow to approach the general classification problem by dividing it in smaller parts, that are then faced by single entities of the network. As a benchmark dataset, the Oxford Flowers 102 dataset is considered, consisting of more than 8000 pictures of flowers belonging to the 102 species commonly found in the UK. These classes sometimes present large variations within them, may be also very similar to one another, and different images of the same subject may differ a lot. The work describes the architecture of the MSNP system, based on modules focusing on a specific class, their training phase, and the evaluation of the model both concerning result accuracy as well as energy consumption. Experimental results on image classification problems show that the model achieves good results, but is strongly connected to image quality, mainly depending on the frequency of images, remarkable changes of pose, images not centered, and subject mostly not shown.

Nonlinear Spiking Neural Systems With Autapses for Predicting Chaotic Time Series.

Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing models that are inspired by the mechanism of spiking neurons and are 3rd-generation neural networks. Chaotic time series forecasting is one of the most challenging problems for machine learning models. To address this challenge, we first propose a nonlinear version of SNP systems, called nonlinear SNP systems with autapses (NSNP-AU systems). In addition to the nonlinear consumption and generation of spikes, the NSNP-AU systems have three nonlinear gate functions, which are related to the states and outputs of the neurons. Inspired by the spiking mechanisms of NSNP-AU systems, we develop a recurrent-type prediction model for chaotic time series, called the NSNP-AU model. As a new variant of recurrent neural networks (RNNs), the NSNP-AU model is implemented in a popular deep learning framework. Four datasets of chaotic time series are investigated using the proposed NSNP-AU model, five state-of-the-art models, and 28 baseline prediction models. The experimental results demonstrate the advantage of the proposed NSNP-AU model for chaotic time series forecasting.

A Prediction Model Based on Gated Nonlinear Spiking Neural Systems

Nonlinear spiking neural P (NSNP) systems are one of neural-like membrane computing models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems have a nonlinear structure and can show rich nonlinear dynamics. In this paper, we introduce a variant of NSNP systems, called gated nonlinear spiking neural P systems or GNSNP systems. Based on GNSNP systems, a recurrent-like model is investigated, called GNSNP model. Moreover, exchange rate forecasting tasks are used as the application background to verify its ability. For the purpose, we develop a prediction model based on GNSNP model, called ERF-GNSNP model. In ERF-GNSNP model, the GNSNP model is followed by a "dense" layer, which is used to capture the correlation between different sub-series in multivariate time series. To evaluate the prediction performance, nine groups of exchange rate data sets are utilized to compare the proposed ERF-GNSNP model with 25 baseline prediction models. The comparison results demonstrate the effectiveness of the proposed ERF-GNSNP model for exchange rate forecasting tasks.

Application of fuzzy spiking neural dP systems in energy coordinated control of multi-microgrid

Application of fuzzy spiking neural dP systems in energy coordinated control of multi-microgrid

An optimization numerical spiking neural P system for solving constrained optimization problems

An optimization spiking neural P (OSN P) system is a discrete optimization model without the aid of evolutionary operators of evolutionary algorithms or swarm intelligence algorithms. However, since the processing object of OSN P systems is a spike, where information is encoded by the timing of spikes or the number of spikes in neurons, OSN P systems are limited for solving continuous optimization problems. To break this limitation, an extended numerical spiking neural (ENSN P) system is proposed based on numerical spiking neural P (NSN P) systems and multiple (ENSN P) systems, called optimization numerical spiking neural P systems (ONSN P systems or ONSNPS), are designed to solve continuous constrained optimization problems. More specifically, in ENSN P systems, the production functions are selected by probability to achieve updated parameters. In OSN P systems, a guider algorithm is introduced to finish individuals’ crossover and selection. The extensively experimental results in five benchmarks, thirty-two optimization problems including five benchmark problems, seventeen manufacturing design optimization problems and ten benchmarks from CEC show that ONSN P systems in this paper outperform or are competitive to twenty-eight optimization algorithms. Finally, algorithm complexity and Holm-Bonferroni procedure based on statistical results is used to test the complexity changing when we use different dimensionality of the search space and the difference in terms of statistical performance. The testing results indicate that the time complexity of ONSN P systems grows linearly with problem dimensions and ONSN P systems are better performance than the most algorithms.

Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity.

Spiking neural networks (SNNs) are the basis for many energy-efficient neuromorphic hardware systems. While there has been substantial progress in SNN research, artificial SNNs still lack many capabilities of their biological counterparts. In biological neural systems, memory is a key component that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning in artificial and SNNs. Here, we propose that Hebbian plasticity is fundamental for computations in biological and artificial spiking neural systems. We introduce a novel memory-augmented SNN architecture that is enriched by Hebbian synaptic plasticity. We show that Hebbian enrichment renders SNNs surprisingly versatile in terms of their computational as well as learning capabilities. It improves their abilities for out-of-distribution generalization, one-shot learning, cross-modal generative association, language processing, and reward-based learning. This suggests that powerful cognitive neuromorphic systems can be built based on this principle.

Enzymatic Numerical Spiking Neural Membrane Systems and their Application in Designing Membrane Controllers.

Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rewriting rules. Inspired by neuronal activities affected by enzymes, a numerical variant of SN P systems called enzymatic numerical spiking neural P systems (ENSNP systems) is proposed wherein each neuron has a set of variables with real values and a set of enzymatic activation-production spiking rules, and each synapse has an assigned weight. By using spiking rules, ENSNP systems can directly implement mathematical methods based on real numbers and continuous functions. Furthermore, ENSNP systems are used to model ENSNP membrane controllers (ENSNP-MCs) for robots implementing wall following. The trajectories, distances from the wall, and wheel speeds of robots with ENSNP-MCs for wall following are compared with those of a robot with a membrane controller for wall following. The average error values of the designed ENSNP-MCs are compared with three recently fuzzy logical controllers with optimization algorithms for wall following. The experimental results showed that the designed ENSNP-MCs can be candidates as efficient controllers to control robots implementing the task of wall following.

Bayesian Integration in a Spiking Neural System for Sensorimotor Control.

The brain continuously estimates the state of body and environment, with specific regions that are thought to act as Bayesian estimator, optimally integrating noisy and delayed sensory feedback with sensory predictions generated by the cerebellum. In control theory, Bayesian estimators are usually implemented using high-level representations. In this work, we designed a new spike-based computational model of a Bayesian estimator. The state estimator receives spiking activity from two neural populations encoding the sensory feedback and the cerebellar prediction, and it continuously computes the spike variability within each population as a reliability index of the signal these populations encode. The state estimator output encodes the current state estimate. We simulated a reaching task at different stages of cerebellar learning. The activity of the sensory feedback neurons encoded a noisy version of the trajectory after actual movement, with an almost constant intrapopulation spiking variability. Conversely, the activity of the cerebellar output neurons depended on the phase of the learning process. Before learning, they fired at their baseline not encoding any relevant information, and the variability was set to be higher than that of the sensory feedback (more reliable, albeit delayed). When learning was complete, their activity encoded the trajectory before the actual execution, providing an accurate sensory prediction; in this case, the variability was set to be lower than that of the sensory feedback. The state estimator model optimally integrated the neural activities of the afferent populations, so that the output state estimate was primarily driven by sensory feedback in prelearning and by the cerebellar prediction in postlearning. It was able to deal even with more complex scenarios, for example, by shifting the dominant source during the movement execution if information availability suddenly changed. The proposed tool will be a critical block within integrated spiking, brain-inspired control systems for simulations of sensorimotor tasks.

On the Tuning of the Computation Capability of Spiking Neural Membrane Systems with Communication on Request.

Spiking neural P systems (abbreviated as SNP systems) are models of computation that mimic the behavior of biological neurons. The spiking neural P systems with communication on request (abbreviated as SNQP systems) are a recently developed class of SNP system, where a neuron actively requests spikes from the neighboring neurons instead of passively receiving spikes. It is already known that small SNQP systems, with four unbounded neurons, can achieve Turing universality. In this context, 'unbounded' means that the number of spikes in a neuron is not capped. This work investigates the dependency of the number of unbounded neurons on the computation capability of SNQP systems. Specifically, we prove that (1) SNQP systems composed entirely of bounded neurons can characterize the family of finite sets of numbers; (2) SNQP systems containing two unbounded neurons are capable of generating the family of semilinear sets of numbers; (3) SNQP systems containing three unbounded neurons are capable of generating nonsemilinear sets of numbers. Moreover, it is obtained in a constructive way that SNQP systems with two unbounded neurons compute the operations of Boolean logic gates, i.e., OR, AND, NOT, and XOR gates. These theoretical findings demonstrate that the number of unbounded neurons is a key parameter that influences the computation capability of SNQP systems.

On Spiking Neural Membrane Systems with Neuron and Synapse Creation.

Spiking neural membrane systems are models of computation inspired by the natural functioning of the brain using the concepts of neurons and synapses, and represent a way of building computational systems of a biological inspiration. A variant of such a model, allowing to create new neurons and synapses during the computation, has been considered in the literature to attack computationally hard problems, like problems in the class NP. In this work, we investigate the computational properties of this variant, by proposing three solutions to computationally hard problems, by models with different features, and comparing them with those present in the literature. In particular, we first propose a nondeterministic solution for the NP-complete problem 3-SAT, by a model using dynamic organization of synapses. Then, we propose a deterministic solution for the same problem, by a model using neuron division and dissolution rules. Finally, we show that dissolution rules are not strictly necessary (by accepting a certain amount of slowdown in computing time), and that also problems beyond the class NP can be solved by systems with neuron division alone.