Evolving reservoir computers reveal bidirectional coupling between predictive power and emergent dynamics

Area of Science:

• Computational neuroscience
• Complex systems science

Background:

• Biological neural networks excel at environmental prediction.
• Understanding these complex computations necessitates considering emergent dynamics.

Purpose of the Study:

• To investigate the relationship between prediction performance and emergent dynamics in neural networks.
• To explore the role of emergence in enabling network-level computational capabilities.

Main Methods:

• Utilized quantitative metrics to measure emergence.
• Modeled environmental time-series prediction using reservoir computing, a bio-inspired framework.
• Examined bidirectional coupling between performance optimization and emergent dynamics.

Main Results:

• Optimizing hyperparameters for prediction performance enhanced emergent dynamics, and vice versa.
• Emergent dynamics were found to be a sufficient and often necessary condition for successful prediction.
• Larger datasets led to stronger emergent dynamics, encoding task-relevant information.

Conclusions:

• A robust bidirectional coupling exists between prediction performance and emergent dynamics in neural networks.
• Emergence-based approaches provide crucial network-level insights, complementing traditional single-neuron analyses.
• Findings highlight the importance of studying emergent dynamics for both biological and artificial neural networks.