# Dendrite Net: A White-Box Module for Classification, Regression, and System Identification

arXiv: https://arxiv.org/abs/2004.03955 · HTML: https://mlsanity.com/paper/2004.03955/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks](https://mlsanity.com/paper/2301.09245/) (2023)
- [One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks](https://mlsanity.com/paper/2303.06316/) (2023)
- [No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks](https://mlsanity.com/paper/2405.02369/) (2024)
- [Generating Unseen Nonlinear Evolution in Sea Surface Temperature Using a Deep Learning-Based Latent Space Data Assimilation Framework](https://mlsanity.com/paper/2412.13477/) (2024)
- [Building a Chinese Medical Dialogue System: Integrating Large-scale Corpora and Novel Models](https://mlsanity.com/paper/2410.03521/) (2024)
- [Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and Normalizing Flows](https://mlsanity.com/paper/2503.03977/) (2025)
- [Dendritic Integration Based Quadratic Neural Networks Outperform Traditional Aritificial Ones](https://mlsanity.com/paper/2307.13609/) (2023)
- [System Filter-Based Common Components Modeling for Cross-Subject EEG Decoding](https://mlsanity.com/paper/2507.05268/) (2025)
- [NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration](https://mlsanity.com/paper/2508.21566/) (2025)
- [Dendritic Convolution for Noise Image Recognition](https://mlsanity.com/paper/2511.18699/) (2025)
- [Memory-DD: A Low-Complexity Dendrite-Inspired Neuron for Temporal Prediction Tasks](https://mlsanity.com/paper/2512.04094/) (2025)

## Abstract (arXiv)

The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This paper presents a basic machine learning algorithm, named Dendrite Net or DD, just like Support Vector Machine (SVM) or Multilayer Perceptron (MLP). DD's main concept is that the algorithm can recognize this class after learning, if the output's logical expression contains the corresponding class's logical relationship among inputs (andbackslashorbackslashnot). Experiments and main results: DD, a white-box machine learning algorithm, showed excellent system identification performance for the black-box system. Secondly, it was verified by nine real-world applications that DD brought better generalization capability relative to MLP architecture that imitated neurons' cell body (Cell body Net) for regression. Thirdly, by MNIST and FASHION-MNIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than Cell body Net for classification. The number of modules can effectively adjust DD's logical expression capacity, which avoids over-fitting and makes it easy to get a model with outstanding generalization capability. Finally, repeated experiments in MATLAB and PyTorch (Python) demonstrated that DD was faster than Cell body Net both in epoch and forward-propagation. The main contribution of this paper is the basic machine learning algorithm (DD) with a white-box attribute, controllable precision for better generalization capability, and lower computational complexity. Not only can DD be used for generalized engineering, but DD has vast development potential as a module for deep learning. DD code is available at GitHub: https://github.com/liugang1234567/Gang-neuron .