Rapid learning of spatial representations for goal-directed navigation based on a novel model of hippocampal place fields. (April 2023)

• Record Type: Journal Article
• Authors: Alabi, Adedapo; Vanderelst, Dieter; Minai, Ali A.
• Is Part Of: Neural networks. Volume 161(2023)
• Page Start: 116
• Page End: 128
• Publication Date: 2023-04
• Subjects: Spatial cognition -- Robotics -- Reinforcement learning -- Hippocampus -- Computational neuroscience; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2023.01.010
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: The discovery of place cells and other spatially modulated neurons in the hippocampal complex of rodents has been crucial to elucidating the neural basis of spatial cognition. More recently, the replay of neural sequences encoding previously experienced trajectories has been observed during consummatory behavior—potentially with implications for rapid learning, quick memory consolidation, and behavioral planning. Several promising models for robotic navigation and reinforcement learning have been proposed based on these and previous findings. Most of these models, however, use carefully engineered neural networks, and sometimes require long learning periods. In this paper, we present a self-organizing model incorporating place cells and replay, and demonstrate its utility for rapid one-shot learning in non-trivial environments with obstacles.