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Updated: Apr 25, 2026

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

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  • 1Department of Computing, Imperial College London, London SW7 2AZ, UK.

Patterns (New York, N.Y.)
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PubMed
Summary

Understanding biological neural networks requires emergent dynamics, where the whole system is more than its parts. Optimizing for prediction performance enhances these dynamics, revealing a bidirectional coupling crucial for network insights.

Keywords:
causal emergencehuman connectomeinformation theorynetwork neuroscienceneuromorphic networkspartial information decompositionrecurrent neural networksreservoir computingsynergytime-series prediction

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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.