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EDLA
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EDLA
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# Error Diffusion Learning Algorithm (EDLA) This repository contains an implementation of the **Error Diffusion Learning Algorithm (EDLA)**, a biologically-inspired neural network training method originally proposed by Kaneko (Kaneko). EDLA features positive and negative neurons interconnected by excitatory and inhibitory synapses and utilizes a global error diffusion mechanism. By diffusing a single global error throughout the network, EDLA simplifies the learning process. ## Repository Contents - `EDLA.py`: Core implementation of the EDLA network architecture. - `EDLA_no_negative_in_the_last_layer.py`: Variant of the EDLA model without negative neurons in the last layer. - `criterion.py` & `criterion_reg.py`: Evaluation functions for trained neural networks, providing criterion values for classification and regression tasks, respectively. - `datasets.py` & `data_reg.py`: Custom dataset loaders for classification and regression tasks, respectively. - `EDLA_digits.ipynb`: Jupyter notebook demonstrating EDLA on image classification tasks (Digits dataset). - `EDLA_regression.ipynb`: Jupyter notebook demonstrating EDLA on regression tasks. ## References - [Kazuhisa Fujita (2026) A Diagnostic Evaluation of Neural Networks Trained with the Error Diffusion Learning Algorithm. Discover Artificial Intelligence. Vol. 6, article number 386.](https://link.springer.com/article/10.1007/s44163-026-01137-y) - Isam Kaneko. 誤差拡散学習法のサンプルプログラム. https://web.archive.org/web/20000306212433/http://village.infoweb.ne.jp/~fwhz9346/ed.htm. ## License This project is licensed under the MIT License.
