Online Program Home
My Program

Abstract Details

Activity Number: 436 - Deep Learning for Data Science
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: WNAR
Abstract #300065 Presentation
Title: Learning Grid Cells with Vector Representation of Self-Position and Matrix Representation of Self-Motion
Author(s): Ying Nian Wu*
Companies: UCLA
Keywords: Grid cells; Representation; Path integral; Path planning; Error correction; Deep learning

We propose a mathematical and computational model for learning the grid cells that are observed in the brains of many species for spatial awareness and navigation. In this model, the self-position of the agent is represented by a vector, and the self-motion of the agent is represented by a block-diagonal matrix. The learned units exhibit hexagon grid patterns that characterize the grid cells. The learned model can be used for path integral and path planning. Moreover, the learned representation is capable of error correction. Based on joint work with Ruiqi Gao, Jianwen Xie and Song-Chun Zhu.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program