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Profile: Chris Eliasmith (University of Waterloo)
  1. Chris Eliasmith (forthcoming). Computational Neuroscience. In Paul R. Thagard (ed.), Philosophy of Psychology and Cognitive Science. Elsevier.
    Keywords: computational neuroscience, neural coding, brain function, neural modeling, cognitive modeling, computation, representation, neuroscience, neuropsychology, semantics, theoretical psychology, theoretical neuroscience.
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  2. Daniel Rasmussen & Chris Eliasmith (2013). God, the Devil, and the Details: Fleshing Out the Predictive Processing Framework. Behavioral and Brain Sciences 36 (3):223-224.
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  3. 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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  4. Chris Eliasmith (2012). The Complex Systems Approach: Rhetoric or Revolution. Topics in Cognitive Science 4 (1):72-77.
    The complex systems approach (CSA) to characterizing cognitive function is purported to underlie a conceptual and methodological revolution by its proponents. I examine one central claim from each of the contributed papers and argue that the provided examples do not justify calls for radical change in how we do cognitive science. Instead, I note how currently available approaches in ‘‘standard’’ cognitive science are adequate (or even more appropriate) for understanding the CSA provided examples.
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  5. Daniel Rasmussen & Chris Eliasmith (2011). A Neural Model of Rule Generation in Inductive Reasoning. Topics in Cognitive Science 3 (1):140-153.
    Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able (...)
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  6. Chris Eliasmith (2010). How We Ought to Describe Computation in the Brain. Studies in History and Philosophy of Science Part A 41 (3):313-320.
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  7. Terrence C. Stewart, Xuan Choo & Chris Eliasmith (2010). Symbolic Reasoning in Spiking Neurons: A Model of the Cortex/Basal Ganglia/Thalamus Loop. In. 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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  8. Chris Eliasmith (2009). Dynamics, Control, and Cognition. In Murat Aydede & P. Robbins (eds.), The Cambridge Handbook of Situated Cognition. Cambridge.
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  9. Terry Stewart & Chris Eliasmith (2009). Compositionality and Biologically Plausible Models. In W. Hinzen, E. Machery & M. Werning (eds.), Oxford Handbook of Compositionality. Oxford.
  10. Chris Eliasmith (2007). How to Build a Brain: From Function to Implementation. Synthese 153 (3):373-388.
    To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach (...)
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  11. Abninder Litt, Chris Eliasmith, Fred Kroon, Steven Weinstein & Paul Thagard (2006). Is the Brain a Quantum Computer? Cognitive Science 30 (3):593-603.
    We argue that computation via quantum mechanical processes is irrelevant to explaining how brains produce thought, contrary to the ongoing speculations of many theorists. First, quantum effects do not have the temporal properties required for neural information processing. Second, there are substantial physical obstacles to any organic instantiation of quantum computation. Third, there is no psychological evidence that such mental phenomena as consciousness and mathematical thinking require explanation via quantum theory. We conclude that understanding brain function is unlikely to require (...)
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  12. Chris Eliasmith (2005). A New Perspective on Representational Problems. Journal of Cognitive Science 6:97-123.
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  13. Chris Eliasmith (2004). Learning Context Sensitive Logical Inference in a Neurobiological Simulation. In Simon D. Levy & Ross Gayler (eds.), Compositional Connectionism in Cognitive Science. Aaai Press. 17--20.
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  14. Chris Eliasmith (2003). Moving Beyond Metaphors: Understanding the Mind for What It Is. Journal of Philosophy 100 (10):493-520.
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  15. Andy Clark & Chris Eliasmith (2002). Philosophical Issues in Brain Theory and Connectionism. In Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition. Mit Press.
  16. Chris Eliasmith (2002). Discreteness and Relevance: A Reply to Roman Poznanski. [REVIEW] Minds and Machines 12 (3):437-438.
  17. Chris Eliasmith (2002). The Myth of the Turing Machine: The Failings of Functionalism and Related Theses. Journal of Experimental and Theoretical Artificial Intelligence 14 (1):1-8.
    The properties of Turing’s famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. Here, I show that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing’s powerful insights regarding computation. In addressing a possible reply to this criticism, I further argue that functionalism is not a useful approach for understanding what it is to have a mind. In particular, I show that the difficulties involved in distinguishing implementation (...)
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  18. Chris Eliasmith & Andy Clark (2002). Philosophical Issues in Brain Theory and Connectionism. In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. Mit Press.
    In this article, we highlight three questions: (1) Does human cognition rely on structured internal representations? (2) How should theories, models and data relate? (3) In what ways might embodiment, action and dynamics matter for understanding the mind and the brain?
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  19. Paul Thagard, Chris Eliasmith, Paul Rusnock & Cameron Shelley (2002). Epistemic Coherence. In R. Elio (ed.), Common sense, reasoning, and rationality. Vancouver Studies in Cognitive Science (Vol. 11). Oxford University Press. 104-131.
    Many contemporary philosophers favor coherence theories of knowledge (Bender 1989, BonJour 1985, Davidson 1986, Harman 1986, Lehrer 1990). But the nature of coherence is usually left vague, with no method provided for determining whether a belief should be accepted or rejected on the basis of its coherence or incoherence with other beliefs. Haack's (1993) explication of coherence relies largely on an analogy between epistemic justification and crossword puzzles. We show in this paper how epistemic coherence can be understood in terms (...)
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  20. Chris Eliasmith (2001). Attractive and in-Discrete: A Critique of Two Putative Virtues of the Dynamicist Theory of Mind. Minds and Machines 11 (3):417-426.
    I argue that dynamicism does not provide a convincing alternative to currently available cognitive theories. First, I show that the attractor dynamics of dynamicist models are inadequate for accounting for high-level cognition. Second, I argue that dynamicist arguments for the rejection of computation and representation are unsound in light of recent empirical findings. This new evidence provides a basis for questioning the importance of continuity to cognitive function, challenging a central commitment of dynamicism. Coupled with a defense of current connectionist (...)
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  21. Chris Eliasmith & Paul Thagard (2001). Integrating Structure and Meaning: A Distributed Model of Analogical Mapping. Cognitive Science 25 (2):245-286.
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  22. Chris Eliasmith (2000). How Neurons Mean: A Neurocomputational Theory of Representational Content. Dissertation, Washington University in St. Louis
    Questions concerning the nature of representation and what representations are about have been a staple of Western philosophy since Aristotle. Recently, these same questions have begun to concern neuroscientists, who have developed new techniques and theories for understanding how the locus of neurobiological representation, the brain, operates. My dissertation draws on philosophy and neuroscience to develop a novel theory of representational content.
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  23. Chris Eliasmith (2000). Is the Brain Analog or Digital? Cognitive Science Quarterly 1 (2):147-170.
    It will always remain a remarkable phenomenon in the history of philosophy, that there was a time, when even mathematicians, who at the same time were philosophers, began to doubt, not of the accuracy of their geometrical propositions so far as they concerned space, but of their objective validity and the applicability of this concept itself, and of all its corollaries, to nature. They showed much concern whether a line in nature might not consist of physical points, and consequently that (...)
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  24. Chris Eliasmith (1998). Dynamical Models and Van Gelder's Dynamicism. Behavioral and Brain Sciences 21 (5):639-639.
    Van Gelder has presented a position which he ties closely to a broad class of models known as dynamical models. While supporting many of his broader claims about the importance of this class (as has been argued by connectionists for quite some time), I note that there are a number of unique characteristics of his brand of dynamicism. I suggest that these characteristics engender difficulties for his view.
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  25. Chris Eliasmith (1998). Review of The Metaphysics of Science. [REVIEW] Dialogue 37 (03):656-.
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  26. Chris Eliasmith (1998). The Metaphysics of Science. Dialogue 37 (3):656-658.
     
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  27. Chris Eliasmith (1998). The Metaphysics of Science: An Account of Modern Science in Terms of Principles, Laws and Theories Craig Dilworth Boston Studies in the Philosophy of Science, Vol. 173 Dordrecht: Kluwer Academic Publishers, 1995, X + 235 Pp., $98.00. [REVIEW] Dialogue 37 (3):656.
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  28. Chris Eliasmith (1997). Computation and Dynamical Models of Mind. Minds and Machines 7 (4):531-41.
    Van Gelder (1995) has recently spearheaded a movement to challenge the dominance of connectionist and classicist models in cognitive science. The dynamical conception of cognition is van Gelder's replacement for the computation bound paradigms provided by connectionism and classicism. He relies on the Watt governor to fulfill the role of a dynamicist Turing machine and claims that the Motivational Oscillatory Theory (MOT) provides a sound empirical basis for dynamicism. In other words, the Watt governor is to be the theoretical exemplar (...)
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  29. Chris Eliasmith, Structure Without Symbols: Providing a Distributed Account of High-Level Cognition.
    There has been a long-standing debate between symbolicists and connectionists concerning the nature of representation used by human cognizers. In general, symbolicist commitments have allowed them to provide superior models of high-level cognitive function. In contrast, connectionist distributed representations are preferred for providing a description of low-level cognition. The development of Holographic Reduced Representations (HRRs) has opened the possibility of one representational medium unifying both low-level and high-level descriptions of cognition. This paper describes the relative strengths and weaknesses of symbolic (...)
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  30. Chris Eliasmith & Paul Thagard (1997). Waves, Particles, and Explanatory Coherence. British Journal for the Philosophy of Science 48 (1):1-19.
    Peter Achinstein (1990, 1991) analyses the scientific debate that took place in the eighteenth and nineteenth centuries concerning the nature of light. He offers a probabilistic account of the methods employed by both particle theorists and wave theorists, and rejects any analysis of this debate in terms of coherence. He characterizes coherence through reference to William Whewell's writings concerning how "consilience of inductions" establishes an acceptable theory (Whewell, 1847) . Achinstein rejects this analysis because of its vagueness and lack of (...)
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  31. Chris Eliasmith (1996). The Third Contender: A Critical Examination of the Dynamicist Theory of Cognition. Philosophical Psychology 9 (4):441-63.
    In a recent series of publications, dynamicist researchers have proposed a new conception of cognitive functioning. This conception is intended to replace the currently dominant theories of connectionism and symbolicism. The dynamicist approach to cognitive modeling employs concepts developed in the mathematical field of dynamical systems theory. They claim that cognitive models should be embedded, low-dimensional, complex, described by coupled differential equations, and non-representational. In this paper I begin with a short description of the dynamicist project and its role as (...)
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