Grid cells and theta as oscillatory interference: theory and predictions

Area of Science:

• Neuroscience
• Computational Neuroscience

Background:

• Grid cells are crucial for spatial navigation and path integration in the brain.
• The oscillatory interference model provides an algorithmic framework for grid cell function.

Purpose of the Study:

• To review the oscillatory interference model as a description of path integration and grid cell implementation.
• To analyze relationships between model variables, theta rhythm, running speed, and intrinsic grid cell firing frequencies.
• To simulate velocity-controlled oscillators (VCOs) with varying preferred directions.

Main Methods:

• Review of the oscillatory interference model.
• Analysis of model variables concerning theta rhythm, running speed, and intrinsic firing frequencies.
• Simulations of velocity-controlled oscillators (VCOs) implemented in neurons with distinct preferred directions.

Main Results:

• Distance traveled is encoded by VCO phase relative to a baseline frequency.
• VCO frequency increases with depolarization from speed-modulated head-direction cells.
• Grid cell firing is driven by VCOs matching motion direction; phase-resetting by place cells prevents error accumulation.
• Baseline frequency relates to local average VCO frequencies; EEG theta frequency comprises zero-speed and speed-dependent components.

Conclusions:

• The model quantitatively predicts inter-relationships between intrinsic firing frequency, grid scale, theta frequency components, and running speed.
• Qualitative predictions are made regarding VCO properties and the influence of environmental novelty on theta, grid scale, and place cell remapping.