A Grid-Density Based Algorithm by Weighted Spiking Neural P Systems with Anti-Spikes and Astrocytes in Spatial Cluster Analysis

Kong, Deting; Wang, Yuan; Wu, Xinyan; Liu, Xiyu; Qu, Jianhua; Xue, Jie

doi:10.3390/pr8091132

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Given the variability in distances between neurons and the misalignment of positions across different brains, we employed a grid‐based approach to investigate the neurons' spatial characteristics. [ 35 , 36 ] We partitioned the regions with measured neurons in area V1 into 6250 evenly distributed grids with the shape of (25, 25, 10) along the X, Y, and Z axes. As the position range on Z was much smaller than the X's and Y's, the Z‐axis's partition number was the neuron locations' original layer number ( Figure 5 A ).…”

Section: Resultsmentioning

confidence: 99%

Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model

Deng,

Schwendeman,

Guan

2024

Advanced Science

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Modeling neuron responses to stimuli can shed light on next‐generation technologies such as brain‐chip interfaces. Furthermore, high‐performing models can serve to help formulate hypotheses and reveal the mechanisms underlying neural responses. Here the state‐of‐the‐art computational model is presented for predicting single neuron responses to natural stimuli in the primary visual cortex (V1) of mice. The algorithm incorporates object positions and assembles multiple models with different train‐validation data, resulting in a 15%–30% improvement over the existing models in cross‐subject predictions and ranking first in the SENSORIUM 2022 Challenge, which benchmarks methods for neuron‐specific prediction based on thousands of images. Importantly, The model reveals evidence that the spatial organizations of V1 are conserved across mice. This model will serve as an important noninvasive tool for understanding and utilizing the response patterns of primary visual cortex neurons.

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Section: Resultsmentioning

confidence: 99%

Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model

Deng,

Schwendeman,

Guan

2024

Advanced Science

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“…SNSbased MIEAs are presented by using tissue-like P systems or neural-like P systems with various network topologies [68]. Various meta-heuristic algorithms, such as GA [69], DE [70] and its variants [71,72], PSO [73,74], ABC [75], and BBO [76], are usually introduced to SNS-based MIEAs as the basic evolutionary operation in the cell or neural [77][78][79][80][81]. The membrane structure in DNS-based MIEAs can be dynamically changed according to communication channel rules, and this class of MIEAs, with an extended membrane structure, has great potential for solving complex problems [82,83].…”

Section: Introductionmentioning

confidence: 99%

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

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An extended membrane system using a tissue-like P system with evolutional symport/antiport rules and a promoter/inhibitor, which is based on the evolutionary mechanism of quantum-behaved particle swarm optimization (QPSO) and improved QPSO, named CQPSO-ETP, is designed and developed in this paper. The purpose of CQPSO-ETP is to enhance the optimization performance of statistical network structure-based membrane-inspired evolutionary algorithms (SNS-based MIEAs) and the QPSO technique. In CQPSO-ETP, evolution rules with a promoter based on a standard QPSO mechanism are introduced to evolve objects, and evolution rules with an inhibitor based on an improved QPSO mechanism using self-adaptive selection, and cooperative evolutionary and logistic chaotic mapping methods, are adopted to avoid prematurity. The communication rules with a promoter/inhibitor for objects are introduced to achieve the exchange and sharing of information between different membranes. Under the control of the evolution and communication mechanism, the CQPSO-ETP can effectively improve the performance with the help of a distributed parallel computing model. The proposed CQPSO-ETP is compared with PSO, QPSO and two existing improved QPSO approaches which are conducted on eight classic numerical benchmark functions to verify the effectiveness. Furthermore, computational experiments which are made on eight tested images with three comparative clustering approaches are adopted, and the experimental results demonstrate the clustering validity of the proposed CQPSO-ETP.

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“…These characteristics mean the SNP systems have good application prospects in solving many practical problems. At present, some scholars have proven the feasibility of SNP systems to solve pattern recognition problems [21][22][23][24][25], combined with algorithms to solve optimization problems [26][27][28], clustering [29], automatic design [30], fault diagnosis [31][32][33][34], and perform arithmetic and logic operations [35][36][37][38], implemented by software and hardware [39,40].…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural Membrane Computing Models

Liu

Ren

2021

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As third-generation neural network models, spiking neural P systems (SNP) have distributed parallel computing capabilities with good performance. In recent years, artificial neural networks have received widespread attention due to their powerful information processing capabilities, which is an effective combination of a class of biological neural networks and mathematical models. However, SNP systems have some shortcomings in numerical calculations. In order to improve the incompletion of current SNP systems in dealing with certain real data technology in this paper, we use neural network structure and data processing methods for reference. Combining them with membrane computing, spiking neural membrane computing models (SNMC models) are proposed. In SNMC models, the state of each neuron is a real number, and the neuron contains the input unit and the threshold unit. Additionally, there is a new style of rules for neurons with time delay. The way of consuming spikes is controlled by a nonlinear production function, and the produced spike is determined based on a comparison between the value calculated by the production function and the critical value. In addition, the Turing universality of the SNMC model as a number generator and acceptor is proved.

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A Grid-Density Based Algorithm by Weighted Spiking Neural P Systems with Anti-Spikes and Astrocytes in Spatial Cluster Analysis

Cited by 5 publications

References 26 publications

Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model

Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

Spiking Neural Membrane Computing Models

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