International Journal of Artificial Intelligence in Education
Cognitive Architectures: Where do we go from here?
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Temporal context as cortical spatial codes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A novel topological map of place cells for autonomous robots
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Artificial neural networks can model cortical local learning and signal processing, but they are not the brain, neither are many special purpose systems to which they contribute. Autonomous mental development models all or part of the brain (or the central nervous system) and how it develops and learns autonomously from infancy to adulthood. Like neural network research, such modeling aims to be biologically plausible. This paper discusses why autonomous development is necessary according to a concept called task muddiness. Then it introduces results for a series of research issues, including the new paradigm for autonomous development, mental architectures, developmental algorithm, a refined classification of types of machine learning, spatial complexity and time complexity. Finally, the paper presents some experimental results for applications, including: vision-guided navigation, object finding, object-based attention (eye-pan), and attention-guided pre-reaching, tour tasks that infants learn to perform early but very perceptually challenging for robots