Remapping in a recurrent neural network model of navigation and context inference

Area of Science:

• Neuroscience
• Computational Neuroscience
• Cognitive Science

Background:

• Neurons in navigational brain regions encode spatial information and remap firing patterns in response to changing contextual factors.
• Understanding how these circuits maintain local computations amid global context shifts is a key challenge.

Approach:

• Recurrent neural network models were trained to perform simultaneous navigation and context inference tasks.
• The models simulated position tracking in environments with transiently-cued context changes.

Key Points:

• The computational models produced activity patterns similar to population-wide remapping observed in the entorhinal cortex.
• The model demonstrates a unified neural circuit solution for integrating navigation and context inference.
• The proposed model generalizes to more complex navigation and inference tasks.

Conclusions:

• Remapping in navigational circuits can be explained by a single neural circuit performing both navigation and context inference.
• This provides a simple, general, and experimentally-grounded model for understanding neural remapping.
• The findings offer insights into how the brain integrates spatial information with contextual cues.