Modeling parietal-premotor interactions in primate control of grasping
Neural Networks - Special issue on neural control and robotics: biology and technology
Spikes: exploring the neural code
Spikes: exploring the neural code
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
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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. 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. relate its own actions with the ones of the demonstrator. In addition, by designing the model to activate the same neural codes during execution and observation, we show how the agent can accomplish observational learning of novel objects.