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

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Neural network training as a dissipative process.

Marco Gori¹, Marco Maggini¹, Alessandro Rossi¹

¹Department of Information Engineering and Mathematics, University of Siena, Italy.

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

|July 9, 2016

Summary

This summary is machine-generated.

This study presents a new theory of learning as a continuous temporal process, inspired by mechanics. It connects learning to energy dissipation and shows its link to gradient descent in neural networks.

Keywords:

Dissipative systems On-line back-propagation Regularization networks Temporal manifolds

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Area of Science:

Artificial Intelligence
Machine Learning
Theoretical Neuroscience

Background:

Learning is often modeled as a discrete process.
Existing frameworks lack a unified temporal perspective.
Intelligent agent interaction with environments is key.

Purpose of the Study:

To propose a theory of learning as a continuous temporal process.
To integrate regularization with temporal dynamics using mechanics principles.
To interpret learning as a dissipative process.

Main Methods:

Introduced the principle of least cognitive action.
Defined counterparts of kinetic and potential energy for cognitive systems.
Applied the theory to supervised learning in neural networks.

Main Results:

Developed a theoretical framework for continuous temporal learning.
Connected Euler-Lagrange equations to gradient descent.
Demonstrated preliminary experimental validation.

Conclusions:

The proposed theory offers a novel perspective on learning.
Learning can be viewed as a dissipative process.
The framework provides a link between mechanics and machine learning algorithms.