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Controlled generation of self-sustained oscillations in complex artificial neural networks
Chang Liu1, Jia-Qi Dong1, Qing-Jian Chen2
1Lanzhou Center for Theoretical Physics and Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou University, Lanzhou, Gansu 730000, China.
Chaos (Woodbury, N.Y.)
|December 9, 2021
Summary
Researchers developed a novel method to control self-sustained oscillations in artificial neural networks using the minimum feedback vertex set (mFVS). This technique allows for generating and switching between diverse neuronal firing patterns for advanced information processing.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Network Science
Background:
- Self-sustained oscillations are crucial for information processing in artificial neural networks.
- Controlling these oscillations in complex networks remains a challenge.
Purpose of the Study:
- To develop a method for inducing and controlling diverse self-sustained oscillatory patterns in artificial neural networks.
- To explore the potential of these networks as prototypes for local, analog processing.
Main Methods:
- Identification of the minimum feedback vertex set (mFVS) to simplify network structure.
- Reintroduction of mFVS nodes to create controlled feedback loops and induce oscillations.
- Exploitation of critical states for flexible pattern switching.
Main Results:
- A method to generate a wide variety of self-sustained oscillatory patterns was established.
- Control over switching between different patterns was demonstrated.
- Neuronal firing patterns with a broad oscillation period distribution were achieved.
Conclusions:
- The mFVS-guided control strategy enables flexible generation and manipulation of oscillatory dynamics in artificial neural networks.
- These networks can serve as prototypes for local, analog processing paradigms.
- The findings offer new possibilities for understanding and engineering complex neural systems.
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