Fast sigmoidal networks via spiking neurons
Neural Computation
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Hebbian learning of pulse timing in the Barn Owl auditory system
Pulsed neural networks
Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
A biologically inspired robotic model for learning by imitation
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Computer and Robot Vision
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
On computing Boolean functions by a spiking neuron
Annals of Mathematics and Artificial Intelligence
Imitation in animals and artifacts
Imitation in animals and artifacts
"Do monkeys ape?": ten years after
Imitation in animals and artifacts
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
Neural Computation
A framework for automating the construction of computational models
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Observational learning based on models of overlapping pathways
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Apprenticeship learning with few examples
Neurocomputing
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Brain imaging studies in macaque monkeys have recently shown that the observation and execution of specific types of grasp actions activate the same regions in the parietal, primary motor and somatosensory lobes. This extended overlapping pathway of activations provides new insights on how primates are able to learn during observation. It suggests that an observed behavior is recognized by simulating it using the circuitry developed for action execution. In the present paper we consider how learning via observation can be implemented in an artificial agent based on the above overlapping pathway of activations. We demonstrate that the circuitry developed for action execution can be activated during observation, if the agent is able to perform action association, i.e. to relate its own actions with the ones of the demonstrator. Following this intuition, a computational model of observation/execution of grasp actions is developed and used in experiments to study its properties and learning capacities. Results show that the agent is able to associate novel objects with known behaviors only by observation. Model investigation after training reveals that, during observation, not only the same regions, but also the same neural representations are activated, implying that an observed action is understood by employing the same neural codes used for its execution.