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Related Experiment Video

Updated: Jun 10, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Delay-derivative-dependent stability for delayed neural networks with unbound distributed delay.

Tao Li¹, Aiguo Song, Shumin Fei

¹School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China. litaodongrui@gmail.com

IEEE Transactions on Neural Networks

|July 30, 2010

Summary

This study presents a new method for ensuring the stability of delayed neural networks with distributed delays. The approach reduces conservatism, offering wider applicability for complex systems.

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Last Updated: Jun 10, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

Neural Network Theory
Control Systems Engineering
Applied Mathematics

Background:

Delayed neural networks are crucial in modeling complex systems but pose stability challenges.
Unbounded distributed delays introduce significant analytical difficulties.
Existing stability criteria often lack applicability due to conservatism.

Purpose of the Study:

To develop a novel, less conservative stability condition for delayed neural networks.
To address the challenge of unbounded distributed delays.
To provide a more broadly applicable stability analysis method.

Main Methods:

Lyapunov-Krasovskii functional approach combined with integral inequalities.
Improved delay-partitioning technique and general convex combination.
Linear Matrix Inequality (LMI)-based criterion dependent on delay bounds and derivative.

Main Results:

A new sufficient condition for global stability of delayed neural networks is derived.
The derived LMI-based criterion is distinct from existing methods, utilizing delay bounds.
Reduced conservatism is demonstrated through numerical examples, enhancing applicability.

Conclusions:

The proposed method effectively guarantees global stability for delayed neural networks with unbounded distributed delays.
The novel approach offers improved performance and wider applicability compared to existing techniques.
Thinning the delay interval is key to achieving reduced conservatism and enhanced stability analysis.