Grid cells: the position code, neural network models of activity, and the problem of learning.
We review progress on the modeling and theoretical fronts in the quest to unravel the computational properties of the grid cell code and to explain the mechanisms underlying grid cell dynamics. The goals of the review are to outline a coherent framework for understanding the dynamics of grid cells and their representation of space; to critically present and draw contrasts between recurrent network models of grid cells based on continuous attractor dynamics and independent-neuron models based on temporal interference; and to suggest open questions for experiment and theory.
Welinder
Peter E
PE
Computation and Neural Systems, California Institute of Technology, Pasadena, California, USA.
Burak
Yoram
Y
Fiete
Ila R
IR
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D018728:Entorhinal Cortex / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D009949:Orientation / Q000502:physiology; D013028:Space Perception / Q000502:physiology