Unified theories of cognition
Engines of the brain: the computational instruction set of human cognition
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
Creating hierarchical categories using cell assemblies
Connection Science
SAL: an explicitly pluralistic cognitive architecture
Journal of Experimental & Theoretical Artificial Intelligence - Pluralism and the Future of Cognitive Science
Emergence of Rules in Cell Assemblies of fLIF Neurons
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Grounding Symbols: Labelling and Resolving Pronoun Resolution with fLIF Neurons
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
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Donald Hebb proposed a hypothesis that specialised groups of neurons, called cell-assemblies (CAs), form the basis for neural encoding of symbols in the human mind. It is not clear, however, how CAs can be re-used and combined to form new representations as in classical symbolic systems. We demonstrate that Hebbian learning of synaptic weights alone is not adequate for all tasks, and that additional meta-control processes should be involved. We describe an earlier proposed architecture (Belavkin & Huyck, 2008) implementing an adaptive conflict resolution process between CAs, and then evaluate it by modelling the probability matching phenomenon in a classic two-choice task. The model and its results are discussed in view of mathematical theory of learning and existing cognitive architectures.