A blackboard architecture for control
Artificial Intelligence
SOAR: an architecture for general intelligence
Artificial Intelligence
Unified theories of cognition
A model of cortical associative memory based on Hebbian cell assemblies
selected papers from the Swedish conference on Connectionism in a broad perspective
Robust reasoning: integrating rule-based and similarity-based reasoning
Artificial Intelligence
Dynamical cell assembly hypothesis—theoretical possibility of spatio-temporal coding in the cortex
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Neuroanatomy in a computational perspective
The handbook of brain theory and neural networks
The Architecture of Cognition
Towards novel neuroscience-inspired computing
Emergent neural computational architectures based on neuroscience
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This chapter discusses reverberating circuits of neurons or Cell Assemblies (CAs) derived from Hebb's [9] proposal. It shows how CAs can quickly categorise an input and make a quick decision when presented with ambiguous data. A categorisation experiment with a computational model of CAs shows that CAs categorise a broad range of patterns.This chapter then describes how CAs might be used to implement the primitives of an symbolic cognitive architecture. It also shows how a system based on CAs is theoretically capable of fast learning, variable binding, rule application, integration with emotion and integration with the external environment.CAs are thus an ideal mechanism for further research into both computational and cognitive neural models. Our medium to long-term plan for exploration of thought via CAs is described.If humans use CAs as a basis of thought, then studying how biological systems use CAs will provide information for computational models. The reverse is also true; computational modelling can direct our research activity in biological neural systems.