1994 Special Issue: A model of hippocampal function
Neural Networks - Special issue: models of neurodynamics and behavior
Affordances, motivations, and the world graph theory
Adaptive Behavior - Special issue on biologically inspired models of navigation
The Neural Simulation Language: A System for Brain Modeling
The Neural Simulation Language: A System for Brain Modeling
Global localization and topological map-learning for robot navigation
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
A Prey Catching and Predator Avoidance Neural-Schema Architecture for Single and Multiple Robots
Journal of Intelligent and Robotic Systems
Robotics and Autonomous Systems
Spatial mapping and map exploitation: a bio-inspired engineering perspective
COSIT'07 Proceedings of the 8th international conference on Spatial information theory
IEEE Transactions on Neural Networks
How universal can an intelligence test be?
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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The study of behavioral and neurophysiological mechanisms involved in rat spatial cognition provides a basis for the development of computational models and robotic experimentation of goal-oriented learning tasks. These models and robotics architectures offer neurobiologists and neuroethologists alternative platforms to study, analyze and predict spatial cognition based behaviors. In this paper we present a comparative analysis of spatial cognition in rats and robots by contrasting similar goal-oriented tasks in a cyclical maze, where studies in rat spatial cognition are used to develop computational system-level models of hippocampus and striatum integrating kinesthetic and visual information to produce a cognitive map of the environment and drive robot experimentation. During training, Hebbian learning and reinforcement learning, in the form of Actor-Critic architecture, enable robots to learn the optimal route leading to a goal from a designated fixed location in the maze. During testing, robots exploit maximum expectations of reward stored within the previously acquired cognitive map to reach the goal from different starting positions. A detailed discussion of comparative experiments in rats and robots is presented contrasting learning latency while characterizing behavioral procedures during navigation such as errors associated with the selection of a non-optimal route, body rotations, normalized length of the traveled path, and hesitations. Additionally, we present results from evaluating neural activity in rats through detection of the immediate early gene Arc to verify the engagement of hippocampus and striatum in information processing while solving the cyclical maze task, such as robots use our corresponding models of those neural structures.