# Progressive Operational Perceptron with Memory

arXiv: https://arxiv.org/abs/1808.06377 · HTML: https://mlsanity.com/paper/1808.06377/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks](https://mlsanity.com/paper/2110.02215/) (2021)
- [Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs](https://mlsanity.com/paper/2109.13604/) (2021)
- [Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks](https://mlsanity.com/paper/2110.02381/) (2021)
- [Blind ECG Restoration by Operational Cycle-GANs](https://mlsanity.com/paper/2202.00589/) (2022)
- [Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression](https://mlsanity.com/paper/2105.12107/) (2021)
- [Super Neurons](https://mlsanity.com/paper/2109.01594/) (2021)
- [Blind Restoration of Real-World Audio by 1D Operational GANs](https://mlsanity.com/paper/2212.14618/) (2022)
- [Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery](https://mlsanity.com/paper/2312.10742/) (2023)
- [High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs](https://mlsanity.com/paper/2407.11040/) (2024)

## Abstract (arXiv)

Generalized Operational Perceptron (GOP) was proposed to generalize the linear neuron model in the traditional Multilayer Perceptron (MLP) and this model can mimic the synaptic connections of the biological neurons that have nonlinear neurochemical behaviours. Progressive Operational Perceptron (POP) is a multilayer network composing of GOPs which is formed layer-wise progressively. In this work, we propose major modifications that can accelerate as well as augment the progressive learning procedure of POP by incorporating an information-preserving, linear projection path from the input to the output layer at each progressive step. The proposed extensions can be interpreted as a mechanism that provides direct information extracted from the previously learned layers to the network, hence the term "memory". This allows the network to learn deeper architectures with better data representations. An extensive set of experiments show that the proposed modifications can surpass the learning capability of the original POPs and other related algorithms.