CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
An Introduction to Neural Networks
An Introduction to Neural Networks
Embodied cognition: a field guide
Artificial Intelligence
Neural Networks - 2004 Special issue: New developments in self-organizing systems
2006 Special issue: Mirror neurons and imitation: A computationally guided review
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Extending the mirror neuron system model, I: Audible actions and invisible grasps
Biological Cybernetics
Eight problems for the mirror neuron theory of action understanding in monkeys and humans
Journal of Cognitive Neuroscience
Learning new motion primitives in the mirror neuron system: a self-organising computational model
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
On the functional contributions of emotion mechanisms to (artificial) cognition and intelligence
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
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When designing artificial intelligent systems, one could do worse, at first glance, than take inspiration from the system whose performance one tries to match: the human brain. The continuing failure to produce such an inspired system is usually blamed on the lack of computational power and/or a lack of understanding of the neuroscience itself. This does not, however, affect the fundamental interest in neuroscience as studying the only known mechanism to date to have produced an intelligent system. This paper adds another consideration (to the well-established observation that our knowledge of how the brain works is sketchy at best) which needs to be taken into account when taking inspiration from neuroscience: the human brain has evolved specifically to serve the human body under constraints imposed by both the body and biological limitations. This does not necessarily imply that it is futile to consider neuroscience in such endeavours; however, this paper argues that one has to view results of neuroscience from a somewhat different perspective to maximise their utility in the creation of artificial intelligent systems and proposes an explicit separation of neural processes into three categories.