Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Feature discovery by competitive learning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Navigating through temporal difference
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Using Hippocampal `Plane Cells' for Navigation, Exploiting Phase Coding
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Covariance learning of correlated patterns in competitive networks
Neural Computation
The role of the hippocampus in solving the Morris water maze
Neural Computation
Navigating with a Focus-Directed Mapping Network
Autonomous Robots
A Planning Map for Mobile Robots: Speed Control and Paths Finding in a Changing Environment
EWLR-8 Proceedings of the 8th European Workshop on Learning Robots: Advances in Robot Learning
Combining Multimodal Sensory Input for Spatial Learning
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Cortico-hippocampal maps and navigation strategies in robots and rodents
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Memory encoding by theta phase precession in the hippocampal network
Neural Computation
Cognitive Map Formation Through Sequence Encoding by Theta Phase Precession
Neural Computation
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
Biomimetic navigation models and strategies in animats
AI Communications
A model of spatial map formation in the hippocampus of the rat
Neural Computation
Model of behavior of rodent hippocampus
ICS'05 Proceedings of the 9th WSEAS International Conference on Systems
Episodes in Space: A Modeling Study of Hippocampal Place Representation
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Predictive models in the brain
Connection Science
Learning anticipation via spiking networks: application to navigation control
IEEE Transactions on Neural Networks
Extraction of distance information from the activity of entorhinal grid cells: a model study
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Simulation of human episodic memory by using a computational model of the hippocampus
Advances in Artificial Intelligence - Special issue on artificial intelligence in neuroscience and systems biology: lessons learnt, open problems, and the road ahead
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Modeling of spatial navigation inspired by rodent hippocampus
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
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
Map-based navigation in mobile robots
Cognitive Systems Research
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The firing rate maps of hippocampal place cells recorded in a freely moving rat are viewed as a set of approximate radial basis functions over the (2-D) environment of the rat. It is proposed that these firing fields are constructed during exploration from ''sensory inputs'' (tuning curve responses to the distance of cues from the rat) and used by cells downstream to construct firing rate maps that approximate any desired surface over the environment. It is shown that, when a rat moves freely in an open field, the phase of firing of a place cell (with respect to the EEG @q rhythm) contains information as to the relative position of its firing field from the rat. A model of hippocampal function is presented in which the firing rate maps of cells downstream of the hippocampus provide a ''population vector'' encoding the instantaneous direction of the rat from a previously encountered reward site, enabling navigation to it. A neuronal simulation, involving reinforcement only at the goal location, provides good agreement with single cell recording from the hippocampal region, and can navigate to reward sites in open fields using sensory input from environmental cues. The system requires only brief exploration, performs latent learning, and can return to a goal location after encountering it only once.