Grid cell modeling with mapping representation of self-motion for path integration

Anonymous

Keywords: Grid cell, path integration, spatial cognition, recurrent neural network

Abstract: The representation of grid cells in the medial entorhinal cortex (MEC) region is crucial for path integration. In this paper, we proposed a grid cell modeling mechanism by mapping the agent’s self-motion in Euclidean space to the neuronal activity of grid cells. Our representational model can achieve hexagonal patterns of grid cells from recurrent neural network (RNN) and enables multi-scale path integration for 1D, 2D and 3D spaces. Different from the existing works which need to learn weights of RNN to get the vector representation of grid cells, our method can obtain weights by direct matrix operations. Moreover, compared with the classical models based on continuous attractor network (CAN), our model avoids the connection matrix’s symmetry limitation and spatial representation redundancy problems. In this paper, we also discuss the connection pattern between grid cells and place cells to demonstrate grid cells’ functioning as a metric for coding space.

One-sentence Summary: A grid cell modeling mechanism for mapping self-motion in euclidean space to grid cells' neuronal activity to achieve RNN-based path integration in both 1D, 2D and 3D spaces and overcome crucial limitations in classical models.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics