2005 Special issue: Robust self-localisation and navigation based on hippocampal place cells
Neural Networks - Special issue: Computational theories of the functions of the hippocampus
Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing
Neural Processing Letters
Design Principles and Constraints Underlying the Construction of Brain-Based Devices
Neural Information Processing
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Map-Based Spatial Navigation: A Cortical Column Model for Action Planning
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Q-Learning Based on Dynamical Structure Neural Network for Robot Navigation in Unknown Environment
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Embodied spatial cognition: Biological and artificial systems
Image and Vision Computing
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Adaptive sensory processing for efficient place coding
Neurocomputing
A cortical column model for multiscale spatial planning
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Comparative Experimental Studies on Spatial Memory and Learning in Rats and Robots
Journal of Intelligent and Robotic Systems
Spatial representation and navigation in a bio-inspired robot
Biomimetic Neural Learning for Intelligent Robots
Robotics and Autonomous Systems
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We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.