A computational model for the formation of grid field based on path integration

Grid cells in the medial entorhinal cortex of rats have special firing characteristics. The cells fire repeatedly and regularly corresponding to a specific spatial location. This is a relatively small space, which is called grid cells' firing fields. Multiple firing fields overlap each other, which finally forms the grid nodes. When the rat reaches any grid nodes in the environment, there exists the corresponding grid cells which discharge maximally. Furthermore, an equilateral triangle is formed by connecting grid nodes and the triangles are covered with the entire environment. Since Hafting et al discovered grid cells in entorhinal cortex of the rat in 2005, researchers have paid more attention to the regular firing structure of grid cells. With the further studying on grid cells, it proved to be that entorhinal cortex plays an important role in path integration. Most importantly, grid cells act as the function of path integrator in the process of path integration. Based on what we have mentioned above, we put forward a computational model for the formation of grid field based on path integration. Our proposed model simulates grid cells' firing fields very well in that the simulation results of our proposed model are in agreement with grid cells' biological experimental electroencephalogram results.

[1]  Chris Eliasmith,et al.  A Controlled Attractor Network Model of Path Integration in the Rat , 2005, Journal of Computational Neuroscience.

[2]  Mark C. Fuhs,et al.  A Spin Glass Model of Path Integration in Rat Medial Entorhinal Cortex , 2006, The Journal of Neuroscience.

[3]  Sandro Romani,et al.  Continuous Attractor Network Model for Conjunctive Position-by-Velocity Tuning of Grid Cells , 2014, PLoS Comput. Biol..

[4]  Lisa M. Giocomo,et al.  Temporal Frequency of Subthreshold Oscillations Scales with Entorhinal Grid Cell Field Spacing , 2007, Science.

[5]  J. O’Keefe,et al.  An oscillatory interference model of grid cell firing , 2007, Hippocampus.

[6]  Mark P. Brandon,et al.  Linking Cellular Mechanisms to Behavior: Entorhinal Persistent Spiking and Membrane Potential Oscillations May Underlie Path Integration, Grid Cell Firing, and Episodic Memory , 2008, Neural plasticity.

[7]  B. McNaughton,et al.  Hippocampectomized rats are capable of homing by path integration. , 1999, Behavioral neuroscience.

[8]  E. Moser,et al.  Spatial representation and the architecture of the entorhinal cortex , 2006, Trends in Neurosciences.

[9]  B. McNaughton,et al.  Self‐motion and the origin of differential spatial scaling along the septo‐temporal axis of the hippocampus , 2005, Hippocampus.

[10]  J. Knierim,et al.  Influence of boundary removal on the spatial representations of the medial entorhinal cortex , 2008, Hippocampus.

[11]  Edvard I Moser,et al.  A metric for space , 2008, Hippocampus.

[12]  J. O’Keefe,et al.  Dual phase and rate coding in hippocampal place cells: Theoretical significance and relationship to entorhinal grid cells , 2005, Hippocampus.

[13]  M. Moser,et al.  Representation of Geometric Borders in the Entorhinal Cortex , 2008, Science.

[14]  Jonathan D. Cohen,et al.  Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex , 2006 .

[15]  M. Hasselmo Grid cell mechanisms and function: Contributions of entorhinal persistent spiking and phase resetting , 2008, Hippocampus.

[16]  Yoram Burakyy,et al.  Accurate Path Integration in Continuous Attractor Network Models of Grid Cells , 2009 .

[17]  H. T. Blair,et al.  Conversion of a phase‐ to a rate‐coded position signal by a three‐stage model of theta cells, grid cells, and place cells , 2008, Hippocampus.

[18]  Neil Burgess,et al.  A metric for the cognitive map: found at last? , 2006, Trends in Cognitive Sciences.

[19]  Emilio Kropff,et al.  Place cells, grid cells, and the brain's spatial representation system. , 2008, Annual review of neuroscience.

[20]  T. Hafting,et al.  Microstructure of a spatial map in the entorhinal cortex , 2005, Nature.

[21]  N. Burgess Grid cells and theta as oscillatory interference: Theory and predictions , 2008, Hippocampus.

[22]  H. T. Blair,et al.  Scale-Invariant Memory Representations Emerge from Moiré Interference between Grid Fields That Produce Theta Oscillations: A Computational Model , 2007, The Journal of Neuroscience.

[23]  Ila R Fiete,et al.  Grid cells: The position code, neural network models of activity, and the problem of learning , 2008, Hippocampus.