5 found
  1. Terrence C. Stewart, Xuan Choo & Chris Eliasmith (2010). Symbolic Reasoning in Spiking Neurons: A Model of the Cortex/Basal Ganglia/Thalamus Loop. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society 1100--1105.
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  2. Paul Thagard & Terrence C. Stewart (2011). The AHA! Experience: Creativity Through Emergent Binding in Neural Networks. Cognitive Science 35 (1):1-33.
    Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity that can support (...)
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    Tobias Schröder, Terrence C. Stewart & Paul Thagard (2014). Intention, Emotion, and Action: A Neural Theory Based on Semantic Pointers. Cognitive Science 38 (5):851-880.
    We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms is (...)
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    Paul Thagard & Terrence C. Stewart (2014). Two Theories of Consciousness: Semantic Pointer Competition Vs. Information Integration. Consciousness and Cognition 30:73-90.
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    Terrence C. Stewart & Chris Eliasmith (2013). Realistic Neurons Can Compute the Operations Needed by Quantum Probability Theory and Other Vector Symbolic Architectures. Behavioral and Brain Sciences 36 (3):307 - 308.
    Quantum probability (QP) theory can be seen as a type of vector symbolic architecture (VSA): mental states are vectors storing structured information and manipulated using algebraic operations. Furthermore, the operations needed by QP match those in other VSAs. This allows existing biologically realistic neural models to be adapted to provide a mechanistic explanation of the cognitive phenomena described in the target article by Pothos & Busemeyer (P&B).
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