Adaptive control of recurrent neural networks using conceptors

Area of Science:

• Machine Learning
• Computational Neuroscience
• Dynamical Systems

Background:

• Recurrent neural networks (RNNs) are powerful for temporal pattern prediction and generation.
• Trained RNNs have static parameters, limiting adaptability to changing conditions post-training.
• External or internal perturbations can degrade the performance of static networks.

Purpose of the Study:

• To investigate the benefits of maintaining network adaptivity after the training phase.
• To enhance the functionality and robustness of RNNs in dynamic environments.
• To explore adaptive control loops for real-time network adjustment.

Main Methods:

• Utilized the conceptor framework to implement adaptive control loops.
• Designed networks with continuously adjusting time-varying internal representations.
• Evaluated adaptive networks on tasks including temporal pattern interpolation, degradation stabilization, and input distortion robustness.

Main Results:

• Adaptive networks demonstrated enhanced performance in temporal pattern interpolation.
• Networks showed improved stabilization capabilities against partial degradation.
• Adaptivity significantly boosted robustness against distorted input signals.

Conclusions:

• Keeping parts of RNNs adaptive post-training enhances functionality and robustness.
• Adaptive control loops enable dynamic adjustment to changing environments.
• This approach broadens the applicability of RNNs in machine learning beyond static, trained models.