A Neuron Model with Dendrite Morphology for Classification

Song, Shuangbao; Chen, Xingqian; Song, Shuangyu; Todo, Yuki

doi:10.3390/electronics10091062

Cited by 16 publications

(8 citation statements)

References 59 publications

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“…In this configuration, BP requires more training time and memory than GBO. The BP algorithm for training LDNM uses the implementation in [35]. The comparison results are reported in Table 2.…”

Section: Comparison With the Bp Algorithmmentioning

confidence: 99%

Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

Song

et al. 2022

Electronics

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The logic dendritic neuron model (LDNM), which is inspired by natural neurons, has emerged as a novel machine learning model in recent years. However, recent studies have also shown that the classification performance of LDNM is restricted by the backpropagation (BP) algorithm. In this study, we attempt to use a heuristic algorithm called the gradient-based optimizer (GBO) to train LDNM. First, we describe the architecture of LDNM. Then, we propose specific neuronal structure pruning mechanisms for simplifying LDNM after training. Later, we show how to apply GBO to train LDNM. Finally, seven datasets are used to determine experimentally whether GBO is a suitable training method for LDNM. To evaluate the performance of the GBO algorithm, the GBO algorithm is compared with the BP algorithm and four other heuristic algorithms. In addition, LDNM trained by the GBO algorithm is also compared with five classifiers. The experimental results show that LDNM trained by the GBO algorithm has good classification performance in terms of several metrics. The results of this study indicate that employing a suitable training method is a good practice for improving the performance of LDNM.

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Section: Comparison With the Bp Algorithmmentioning

confidence: 99%

Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

Song

et al. 2022

Electronics

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“…Its effectiveness in classification, approximation, and prediction has been demonstrated in various studies, including refs. [17, 19–40].…”

Section: Introductionmentioning

confidence: 99%

Pruning method for dendritic neuron model based on dendrite layer significance constraints

Luo

Wen

et al. 2023

CAAI Trans on Intel Tech

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The dendritic neural model (DNM) mimics the non-linearity of synapses in the human brain to simulate the information processing mechanisms and procedures of neurons. This enhances the understanding of biological nervous systems and the applicability of the model in various fields. However, the existing DNM suffers from high complexity and limited generalisation capability. To address these issues, a DNM pruning method with dendrite layer significance constraints is proposed. This method not only evaluates the significance of dendrite layers but also allocates the significance of a few dendrite layers in the trained model to a few dendrite layers, allowing the removal of low-significance dendrite layers. The simulation experiments on six UCI datasets demonstrate that our method surpasses existing pruning methods in terms of network size and generalisation performance.

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“…For solving a generalized large-scale classification problem, Jia et al suggested a reconciliation method with DNM by using a particle antagonism mechanism, and Ji et al proposed a DNM-based multiobjective evolutionary algorithm [ 25 ]. In terms of feature selection, Song et al addressed the high-dimensional challenge [ 26 ], and Gao et al also showed the expansibility and flexibility of DNM for diverse applications [ 27 ]. Utilizing the multiplication operation that is useful to the information processing for a single neuron, the computing in synapses is imaginatively described using sigmoid functions.…”

Section: Introductionmentioning

confidence: 99%

An Extension Network of Dendritic Neurons

Peng

Gao

Wang

et al. 2023

Computational Intelligence and Neuroscience

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Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch–Pitts neuron unit. A widely recognized biologically plausible dendritic neuron model (DNM) has demonstrated its effectiveness in alleviating the aforementioned issues, but it can only solve binary classification tasks, which significantly limits its applicability. In this study, a novel extended network based on the dendritic structure is innovatively proposed, thereby enabling it to solve multiple-class classification problems. Also, for the first time, an efficient error-back-propagation learning algorithm is derived. In the extensive experimental results, the effectiveness and superiority of the proposed method in comparison with other nine state-of-the-art classifiers on ten datasets are demonstrated, including a real-world quality of web service application. The experimental results suggest that the proposed learning algorithm is competent and reliable in terms of classification performance and stability and has a notable advantage in small-scale disequilibrium data. Additionally, aspects of network structure constrained by scale are examined.

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A Neuron Model with Dendrite Morphology for Classification

Cited by 16 publications

References 59 publications

Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

Training a Logic Dendritic Neuron Model with a Gradient-Based Optimizer for Classification

Pruning method for dendritic neuron model based on dendrite layer significance constraints

An Extension Network of Dendritic Neurons

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