Trained stochastic model of biological neural network used in image processing task

DOI10.1016/j.amc.2014.12.082zbMath1410.94018OpenAlexW2002850781MaRDI QIDQ669777

Aleksandra Swietlicka

Publication date: 15 March 2019

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2014.12.082

zbMATH Keywords

image processingHodgkin-Huxley modelLagrange multipliers methoddendritic structuredescent gradient method

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (3)

Finite-time resilient \(H_\infty\) state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism ⋮ Investigation of generalization ability of a trained stochastic kinetic model of neuron ⋮ Regularization theory in the study of generalization ability of a biological neural network model

Cites Work

• Linear and nonlinear programming.
• Mathematical physiology
• Spiking Neuron Models
• Nonlinear Programming
• Unnamed Item
• Unnamed Item

This page was built for publication: Trained stochastic model of biological neural network used in image processing task