A neuron model with fluid properties for solving labyrinthian puzzle
Biological Cybernetics
Proceedings of the seventh international conference (1990) on Machine learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Self-Organizing Maps
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A neuromorphic model of spatial lookahead planning
Neural Networks
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Recent experimental evidence indicates that animals can use mental simulation to make decisions about the actions to take during goal-directed navigation. The principal brain areas found to be active during this process are the hippocampus, the ventral striatum and the sensory-motor cortex. In this paper, we present a computational model that includes biological aspects of this circuit and explains mechanistically how it may be used to imagine and evaluate future events. Its most salient characteristic is that choices about actions are made by simulating movements and their sensory effects using the same brain areas that are active during overt execution. More precisely, the simulation of an action (e.g., walking) creates a new sensory pattern that is evaluated in the same way as real inputs. The model is validated in a navigation task in which a simulated rat is placed in a complex maze. We show that hippocampal and striatal cells are activated to simulate paths, to retrieve their estimated value and to make decisions. We link these results with a general framework that sees the brain as a predictive device that can 'detach' itself from the here-and-now of current perception using mechanisms such as episodic memories, motor and visual imagery.