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Related Experiment Video

Updated: Nov 9, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Self-organized Operational Neural Networks with Generative Neurons.

Serkan Kiranyaz¹, Junaid Malik², Habib Ben Abdallah¹

¹Electrical Engineering, College of Engineering, Qatar University, Qatar.

Neural Networks : the Official Journal of the International Neural Network Society

|April 15, 2021

Summary

Self-organized Operational Neural Networks (Self-ONNs) introduce generative neurons that adapt operators during training, overcoming limitations of conventional networks. This approach enhances computational efficiency and network diversity for improved performance.

Keywords:

Convolutional Neural Networks Generative neurons Heterogeneous networks Operational Neural Networks

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Conventional Convolutional Neural Networks (CNNs) suffer from homogeneity and a limited linear neuron model.
Operational Neural Networks (ONNs) offer heterogeneity but face computationally demanding operator search and limited adaptability.
Current ONNs rely on fixed operator libraries, risking performance degradation if optimal operators are absent.

Purpose of the Study:

To introduce Self-organized Operational Neural Networks (Self-ONNs) for enhanced network diversity and computational efficiency.
To enable generative neurons that adapt nodal operators during training, eliminating the need for a predefined operator set.
To develop an effective error back-propagation method for Self-ONNs.

Main Methods:

Proposed Self-organized ONNs (Self-ONNs) with generative neurons capable of optimizing nodal operators.
Implemented an error back-propagation mechanism tailored for the operational layers of Self-ONNs.
Conducted experiments on four challenging problems to evaluate Self-ONNs against conventional ONNs and CNNs.

Main Results:

Self-ONNs demonstrated superior learning capability compared to conventional ONNs and CNNs.
The proposed method significantly improved computational efficiency.
Adaptive nodal operators led to greater network diversity and performance.

Conclusions:

Self-ONNs offer a more effective and efficient approach to deep learning by enabling dynamic operator adaptation.
The Self-ONN architecture overcomes the limitations of fixed operator sets and computationally intensive search methods.
This research paves the way for more diverse and performant neural network architectures.