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Episodic memory is fundamental to the brain's cognitive function, but how neuronal activity is temporally organized during its encoding and retrieval is still unknown. In this article, combining hippocampus structure with a spiking neural network (SNN), a new bionic spiking temporal memory (BSTM) model is proposed to explore the encoding, formation, and retrieval of episodic memory. For encoding episodic memory, the spike-timing-dependent-plasticity (STDP) learning algorithm and a proposed minicolumn selection algorithm are used to encode each input item into several active minicolumns. For the formation of episodic memory, a sequential memory algorithm is proposed to store the contexts between items. For retrieval of episodic memory, the local retrieval algorithm and the global retrieval algorithm are proposed to retrieve sequence information, achieving multisentence prediction and multitime step prediction. All functions of BSTM are based on bionic spiking neurons, which have biological characteristics including columnar and dendritic structures, firing and receiving spikes, and delaying transmission. To test the performance of the BSTM model, the Children's Book Test (CBT) data set was used to conduct a series of experiments under different settings, including changing the number of minicolumns, neurons and sequences, modifying sequence items, etc. Compared to other sequence memory algorithms, the experimental results show that the proposed BSTM achieves higher accuracy and better robustness.

The brain-inspired spiking neural networks (SNNs) hold the advantages of lower power consumption and powerful computing capability. However, the lack of effective learning algorithms has obstructed the theoretical advance and applications of SNNs. The majority of the existing learning algorithms for SNNs are based on the synaptic weight adjustment. However, neuroscience findings confirm that synaptic delays can also be modulated to play an important role in the learning process. Here, we propose a gradient descent-based learning algorithm for synaptic delays to enhance the sequential learning performance of single spiking neuron. Moreover, we extend the proposed method to multilayer SNNs with spike temporal-based error backpropagation. In the proposed multilayer learning algorithm, information is encoded in the relative timing of individual neuronal spikes, and learning is performed based on the exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. Experimental results on both synthetic and realistic datasets show significant improvements in learning efficiency and accuracy over the existing spike temporal-based learning algorithms. We also evaluate the proposed learning method in an SNN-based multimodal computational model for audiovisual pattern recognition, and it achieves better performance compared with its counterparts.

Associative system has attracted increasing attention for it can store basic information and then infer details to match perception with an efficient self-organization algorithm. However, the implementation of the associative system with the application of real-world data is relatively difficult. To address this issue, we propose a novel biologically inspired auto-associative (BIAA) network to explore the structure, encoding and formation of associative memory as well as to extend the ability to real-world application. Our network is constructed by imitating the organization of the cortical minicolumns where each minicolumn contains plenty of parallel biological spiking neurons. To allow the network to learn and predict one symbol per theta cycle, we incorporate synaptic delay and theta oscillation into the neuron dynamic process. Subsequently, we design a sparse temporal population (STP) coding scheme that allows each input symbol to be represented as stable, unique, and easily recallable sparsely distributed representations. By combining associative learning dynamics with the STP coding, our network realizes efficient storage and inference in an ordered manner. Experimental results indicate that the proposed network successfully performs sequence retrieval from partial text and sequence recovery from distorted information. BIAA network provides new insight into introducing biologically inspired mechanisms into associative system and has enormous potential for hardware and software applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit information, which are not only biologically realistic but also suitable for ultralow-power event-driven neuromorphic implementation. Just like other deep learning techniques, deep SNNs (DeepSNNs) benefit from the deep architecture. However, the training of DeepSNNs is not straightforward because the well-studied error backpropagation (BP) algorithm is not directly applicable. In this article, we first establish an understanding as to why error BP does not work well in DeepSNNs. We then propose a simple yet efficient rectified linear postsynaptic potential function (ReL-PSP) for spiking neurons and a spike-timing-dependent BP (STDBP) learning algorithm for DeepSNNs where the timing of individual spikes is used to convey information (temporal coding), and learning (BP) is performed based on spike timing in an event-driven manner. We show that DeepSNNs trained with the proposed single spike time-based learning algorithm can achieve the state-of-the-art classification accuracy. Furthermore, by utilizing the trained model parameters obtained from the proposed STDBP learning algorithm, we demonstrate ultralow-power inference operations on a recently proposed neuromorphic inference accelerator. The experimental results also show that the neuromorphic hardware consumes 0.751 mW of the total power consumption and achieves a low latency of 47.71 ms to classify an image from the Modified National Institute of Standards and Technology (MNIST) dataset. Overall, this work investigates the contribution of spike timing dynamics for information encoding, synaptic plasticity, and decision-making, providing a new perspective to the design of future DeepSNNs and neuromorphic hardware.

This article is devoted to the H ∞ estimation problem for stochastic semi-Markovian switching complex-valued neural networks subject to incomplete measurement outputs, where the time-varying delay also depends on another semi-Markov process. A sequence of random variables with known statistical property is introduced to depict the missing measurement phenomenon. Based on the generalized Itoˆ's formula in complex form concerning with the semi-Markovian systems, complex-valued reciprocal convex inequality as well as intensive stochastic analysis method, some mode-dependent sufficient conditions are presented guaranteeing the estimation error system to be exponentially mean-square stable with a prespecified H ∞ disturbance attenuation level. In addition, the mode-dependent estimator gain matrices are appropriately designed according to the feasible solutions of certain complex matrix inequalities. In the end, one numerical example is provided to illustrate effectiveness of the theoretical results., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

To investigate the local micro-/nanoscale region in a large scale sample, an image reconstruction method for nanometer computed tomography (nano-CT) was proposed in this paper. In the algorithm, wavelets were used to localize the filtered-backprojection (FBP) algorithm because of its space-frequency localization property. After the implementation of the algorithm, two simulation local reconstruction experiments were performed to confirm its effectiveness. Three evaluation criteria were used in the experiments to judge the quality of the reconstructed images. The experimental results showed that the algorithm proposed in this paper performed best because (1) the quality of its results had improved 20%-30% compared to the results of FBP and 10%-30% compared to the results of another wavelet algorithm; (2) the new algorithm was stable under different circumstances. Besides, an actual reconstruction experiment was performed using real projection data that had been collected in a CT experiment. Two-dimensional (2D) and three-dimensional (3D) images of the sample were reconstructed. The microstructure of the sample could be clearly observed in the reconstructed images. Since much attention has been directed towards the nano-CT technique to investigate the microstructure of materials, this new wavelet-based local tomography algorithm could be considered as a meaningful effort.

Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.

A method of retrieving thermophysical depth profiles of continuously inhomogeneous materials is presented both theoretically and experimentally using laser infrared photothermal radiometry. This method represents the three-dimensional (3D) extension of earlier one-dimensional thermal-wave inverse-problem techniques for reconstructing inhomogeneous thermal-conductivity or diffusivity depth profiles. A 3D theoretical model suitable for characterizing solids with arbitrary continuously varying thermophysical property depth profiles and finite (collimated or focused) laser beam spotsize is developed. A numerical fitting algorithm to retrieve the thermophysical profile was demonstrated with three case hardened steel samples. The reconstructed thermal conductivity depth profiles were found to be well anticorrelated with microhardness profiles obtained with the conventional indenter method. [ABSTRACT FROM AUTHOR]

It is crucial to represent the semantic information of a document in sentiment classification. Various semantic information representation models have been proposed, however existing approaches have their setbacks. Notable weaknesses among these are: (1) tradition VSM methods, completely ignore the semantic information; (2) averaging word embedding methods, cannot depict the synthetical semantic meaning of the given document; (3) neural network methods, require complex structure and are notoriously difficult to be trained. To overcome these limitations, we introduce a simple but novel method which we call bag of meta-words (BoMW). In our method, the semantic information of the document is indicated by a meta-words vector in which every single meta-word element denotes particular semantic information. Especially, these meta-words are extracted from pre-trained word embeddings through two different but complemental models, naive interval meta-words (NIM) and feature combination meta-words (FCM). In general, our new model BoMW is as simple as traditional VSM model but it can capture the synthetical semantic meanings of the document. Numerous experiments on two benchmarks (IMDB dataset and Pang’s dataset) are carried out to verify the effectiveness of the proposed method, and the results show that the performance of our method can exceed the traditional VSM methods and methods using pre-trained word embedding. [ABSTRACT FROM AUTHOR]

The single-pair K shortest path (KSP) problem can be described as finding $$k$$ least cost paths through a graph between two given nodes in a non-decreasing order, while single-source KSP algorithms aim to find KSPs from a given node to each other node. However, little effort has been devoted to the single-source KSP approaches. This paper proposes a novel single-source KSP algorithm in a given directed weighted graph where loops are allowed. The proposed method is designed to compute a set of shortest paths with exactly $$k$$ distinctive lengths in a non-decreasing order. Meanwhile, it can also find all shortest paths with the length less than a given threshold. Inspired by water flowing principle, we imagine that there are waters flowing from a source node to each other node along edges at a constant speed. When the water reaches a node, the node will generate new waters flowing along its outgoing edges. By stepping back the traces of the water, the ordered shortest paths can be obtained. We also address the correctness and effectiveness of the method. Simulations are carried out using synthetic data and practical graph data, which demonstrate the considerable performance of the proposed approach especially for single-source KSP problems. [ABSTRACT FROM AUTHOR]

Abstract: Shortest-path problem is well-known optimization problem and has been studied by many authors in recent years. Typically it is solved by using the famous Dijkstra''s algorithm, which would quickly provide a global optimization solution in most instances. However, as the problem scale increases, this method is inefficient and may consume a considerable amount of CPU time. Neural networks, which are massively parallel models, can solve this question easily. This paper presents a novel biological neural network based algorithm for the finding of the shortest path in large scale systems. The start neuron fires first, and then the firing event spreads out through the lateral connections among the neurons, like the propagation of a wave. Then the generated spiking wave spreads at a constant speed so that the time of travel between two neurons is proportional to the path length between them. The computational complexity of the algorithm is only related to the length of the shortest path, and independent of the number of existing paths in the graph. Simulation results show that the proposed method is more efficient than Dijkstra''s in the larger scale systems. [Copyright &y& Elsevier]