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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Understanding how neural systems achieve both behavioral robustness and flexibility is a fundamental challenge.
  • Neural population dynamics are thought to underlie complex behaviors, but the specific mechanisms for balancing stability and adaptability remain elusive.

Purpose of the Study:

  • To investigate how neural population dynamics support robust yet adaptable goal-directed navigation.
  • To explore the role of chaotic dynamics in flexible behavior using a data-driven modeling approach.

Main Methods:

  • Combined large-scale calcium imaging from mouse cortex with a novel data-derived modeling framework.
  • Trained agents in a simulative environment to recapitulate mouse neural and behavioral data.
  • Analyzed circuit-level mechanisms and performed perturbations in the data-derived agents.

Main Results:

  • Discovered novel chaotic attractor dynamics within the data-derived agents.
  • These dynamics exhibited intrinsically variable trajectories within goal-specific attracting landscapes.
  • Demonstrated that these chaotic dynamics support reliable goal achievement and structured behavioral variability.
  • Identified mechanisms for stabilizing chaos and enhancing behavioral adaptability.

Conclusions:

  • Chaos serves as a functional principle for flexible behavior in goal-directed navigation.
  • Closed-loop interactions between behavior and neural dynamics are crucial for understanding this principle.
  • The data-derived modeling approach provides new insights into neural mechanisms of behavioral flexibility.