Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Affordances, motivations, and the world graph theory
Adaptive Behavior - Special issue on biologically inspired models of navigation
Exploration, navigation and cognitive mapping
Adaptive Behavior
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Neural Simulation Language: A System for Brain Modeling
The Neural Simulation Language: A System for Brain Modeling
Population coding and decoding in a neural field: a computational study
Neural Computation
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Learning spatial concepts from RatSLAM representations
Robotics and Autonomous Systems
Persistent Navigation and Mapping using a Biologically Inspired SLAM System
International Journal of Robotics Research
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It is often suggested that the place cells of the hippocampus (and more recently, the grid cells of the entorhinal cortex) furnish a cognitive map. However, this can only be part of the story: (1) the â聙聹hippocampal chartâ聙聺 provided by place cells and grid cells differs radically when a rat is placed in different environments and so a higher level organization is needed to link these charts into an overall cognitive map of the rat's world; and (2) even without a hippocampus a rat can exploit much of the spatial structure of its world. The World Graph (WG) model addressed the former problem, whereas the Taxon Affordance Model (TAM) was developed to address the latter, with the two models being integrated to form the TAM-WG model. Here we provide a new version strengthened in three ways: (1) we relate the TAM model to explicit ideas of executability and desirability; (2) we show how temporal difference learning elegantly supplies â聙聹spatial difference learningâ聙聺 to resolve the debate between the local hypothesis and the non-local hypothesis for node selection in the original WG model; and (3) we analyze an explicit example of how the â聙聹locometric mapâ聙聺 provided by grid cells and place cells can complement the high-level cognitive map given by the WG, demonstrating the importance of navigation algorithms that integrate across multiple levels of spatial organization.