Search

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.)

Protein remote homology detection is one of the most fundamental research tool for protein structure and function prediction. Most search methods for protein remote homology detection are evaluated based on the Structural Classification of Proteins-extended (SCOPe) benchmark, but the diverse hierarchical structure relationships between the query protein and candidate proteins are ignored by these methods. In order to further improve the predictive performance for protein remote homology detection, a search framework based on the predicted protein hierarchical relationships (PHR-search) is proposed. In the PHR-search framework, the superfamily level prediction information is obtained by extracting the local and global features of the Hidden Markov Model (HMM) profile through a convolution neural network and it is converted to the fold level and class level prediction information according to the hierarchical relationships of SCOPe. Based on these predicted protein hierarchical relationships, filtering strategy and re-ranking strategy are used to construct the two-level search of PHR-search. Experimental results show that the PHR-search framework achieves the state-of-the-art performance by employing five basic search methods, including HHblits, JackHMMER, PSI-BLAST, DELTA-BLAST and PSI-BLASTexB. Furthermore, the web server of PHR-search is established, which can be accessed at http://bliulab.net/PHR-search., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Microscopic images of bovine milk somatic cells are used to classify neutrophils, epithelial cells, macrophages and lymphocytes. Using pattern recognition technology, the problem of classification and recognition is solved from different nature, levels and spaces. The proposed RKSGA-SVM algorithm is used to realize somatic cell image recognition. First, color, morphological and texture features of four types of cells are extracted separately, including geometric and moment invariant features. Second, ReliefF algorithm is used to calculate the weights of all features. According to preset cumulative contribution rate, the preliminary feature set is obtained. Third, redundant features are eliminated by Kolmogorov–Smirnov (KS) test, and the high-level optimization is obtained. The selected feature sets have remarkable distinguishing ability. Finally, the weighted optimal feature sets are obtained by weighted coefficient method on the advanced optimal feature sets. The overall accuracy of RKSGA-SVM algorithm is 99.00%, and Kappa coefficient is 0.987. The proposed algorithm has the advantages of balancing classification accuracy, eliminating redundancy and reducing feature dimension. On the premise of ensuring high classification accuracy, the feature set can reduce feature dimension and the amount of data calculation, improve operation efficiency and save storage space. Experiments show that the feature selection method proposed in this paper is feasible and more suitable for extracting feature sets in the process of somatic cell classification. [ABSTRACT FROM AUTHOR]

Although transit stop location problem has been extensively studied, the two main categories of modeling methodologies, i.e., discrete models and continuum approximation (CA) ones, seem have little intersection. Both have strengths and weaknesses, respectively. This study intends to integrate them by taking the advantage of CA models' parsimonious property and discrete models' fine consideration of practical conditions. In doing so, we first employ the state-of-the-art CA models to yield the optimal design, which serves as the input to the next discrete model. Then, the stop location problem is formulated into a multivariable nonlinear minimization problem with a given number of stop location variables and location constraint. The interior-point algorithm is presented to find the optimal design that is ready for implementation. In numerical studies, the proposed model is applied to a variety of scenarios with respect to demand levels, spatial heterogeneity, and route length. The results demonstrate the consistent advantage of the proposed model in all scenarios as against its counterparts, i.e., two existing recipes that convert CA model-based solution into real design of stop locations. Lastly, a case study is presented using real data and practical constraints for the adjustment of a bus route in Chengdu (China). System cost saving of 15.79% is observed by before-and-after comparison. [ABSTRACT FROM AUTHOR]