Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Why interaction is more powerful than algorithms
Communications of the ACM
The handbook of brain theory and neural networks
Visual scene perception: neurophysiology
The handbook of brain theory and neural networks
An agent based approach to site selection for wireless networks
Proceedings of the 2002 ACM symposium on Applied computing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
High-Level Connectionist Models
High-Level Connectionist Models
Self-Localisation in the ‘Senario’ Autonomous Wheelchair
Journal of Intelligent and Robotic Systems
Dynamic Knowledge Representation in Connectionist Systems
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Polychronization: Computation with Spikes
Neural Computation
Challenge problems for artificial intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
COMUNICA: a question answering system for Brazilian Portuguese
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations
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An information processing paradigm in the brain is proposed, instantiated in an artificial neural network using biologically motivated temporal encoding. The network will locate within the external world stimulus, the target memory, defined by a specific pattern of micro-features. The proposed network is robust and efficient. Akin in operation to the swarm intelligence paradigm, stochastic diffusion search, it will find the best-fit to the memory with linear time complexity. Information multiplexing enables neurons to process knowledge as 'tokens' rather than 'types'. The network illustrates possible emergence of cognitive processing from low level interactions such as memory retrieval based on partial matching.