Artificial Intelligence - On connectionist symbol processing
Replicator equations, maximal cliques, and graph isomorphism
Neural Computation
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
Neural Engineering (Computational Neuroscience Series): Computational, Representation, and Dynamics in Neurobiological Systems
The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
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In this position paper we argue that BICA must simultaneously be compatible with the explanation of human cognition and support the human design of artificial cognitive systems. Most cognitive neuroscience models fail to provide a basis for implementation because they neglect necessary levels of functional organisation in jumping directly from physical phenomena to cognitive behaviour. Of those models that do attempt to include the intervening levels, most either fail to implement the required cognitive functionality or do not scale adequately. We argue that these problems of functionality and scaling arise because of identifying computational entities with physical resources such as neurons and synapses. This issue can be avoided by introducing appropriate virtual machines. We propose a tool stack that introduces such virtual machines and supports design of cognitive architectures by simplifying the design task through vertical modularity.