-
Compositionality and Biologically Plausible ModelsIn Markus Werning, Wolfram Hinzen & Edouard Machery (eds.), The Oxford Handbook of Compositionality, Oxford University Press. 2012.Cognitive theories have expressed their components using an artificial symbolic language, such as first-order predicate logic, and the atoms in such representations are non-decomposable letter strings. A neural theory merely demonstrates how to implement a classical symbol system using neurons: this is actually an argument against the importance of the neural description. The fact that symbol systems are physically instantiated in neurons becomes a mere implementational detail, since there is a …Read more
-
216Philosophical issues in brain theory and connectionismIn Michael A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Second Edition, Mit Press. 2002.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?
-
16Connecting Biological Detail With Neural Computation: Application to the Cerebellar Granule–Golgi MicrocircuitTopics in Cognitive Science 13 (3): 515-533. 2021.We present techniques for integrating low‐level neurobiological constraints into high‐level, functional cognitive models. In particular, we use these techniques to construct a model of eyeblink conditioning in the cerebellum based on temporal representations in the recurrent Granule‐Golgi microcircuit.
-
32CUE: A unified spiking neuron model of short-term and long-term memoryPsychological Review 128 (1): 104-124. 2021.
-
16How to build a brain: from function to implementationSynthese 159 (3): 373-388. 2007.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 to addressing both…Read more
-
10A Spiking Neuron Model of Word Associations for the Remote Associates TestFrontiers in Psychology 8. 2017.
-
26The Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working MemoryTopics in Cognitive Science 9 (1): 117-134. 2017.Duggins et al. use a spiking neural network model of working memory to predict the reaction to two drugs known to affect working memory (guanfacine and phenylephrine). The model can explain data from moneys at the biophysical, neural, and behavioral levels.
-
14Improving With Practice: A Neural Model of Mathematical DevelopmentTopics in Cognitive Science 9 (1): 6-20. 2016.The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. Although gradual behavioral improvements from practice have been modeled in spiking neural networks, few such models have attempted to explain cognitive development of a task as complex as addition. In this work, we model the progression from a counting-based strategy for addition to a recall-based strategy. The model consists of two networks working in para…Read more
-
38Using Neural Networks to Generate Inferential Roles for Natural LanguageFrontiers in Psychology 8. 2018.
-
113Attractive and in-discrete: A critique of two putative virtues of the dynamicist theory of mindMinds and Machines 11 (3): 417-426. 2001.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 co…Read more
-
Compositionality and biologically plausible modelsIn Markus Werning, Wolfram Hinzen & Edouard Machery (eds.), The Oxford Handbook of Compositionality, Oxford University Press. 2012.
-
219The myth of the Turing machine: The failings of functionalism and related thesesJournal of Experimental and Theoretical Artificial Intelligence 14 (1): 1-8. 2002.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 sh…Read more
-
69Integrating structure and meaning: a distributed model of analogical mappingCognitive Science 25 (2): 245-286. 2001.In this paper we present Drama, a distributed model of analogical mapping that integrates semantic and structural constraints on constructing analogies. Specifically, Drama uses holographic reduced representations (Plate, 1994), a distributed representation scheme, to model the effects of structure and meaning on human performance of analogical mapping. Drama is compared to three symbolic models of analogy (SME, Copycat, and ACME) and one partially distributed model (LISA). We describe Drama's p…Read more
-
174Computational neuroscienceIn Paul Thagard (ed.), Handbook of the Philosophy of Psychology and Cognitive Science, Elsevier. 2006.<b>Keywords</b>: computational neuroscience, neural coding, brain function, neural modeling, cognitive modeling, computation, representation, neuroscience, neuropsychology, semantics, theoretical psychology, theoretical neuroscience.
-
78Concepts as Semantic Pointers: A Framework and Computational ModelCognitive Science 40 (5): 1128-1162. 2016.The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental rep…Read more
-
105Is the brain a quantum computer?Cognitive Science 30 (3): 593-603. 2006.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 expl…Read more
-
349How Neurons Mean: A Neurocomputational Theory of Representational ContentDissertation, Washington University in St. Louis. 2000.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
-
95Realistic neurons can compute the operations needed by quantum probability theory and other vector symbolic architecturesBehavioral and Brain Sciences 36 (3). 2013.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)
-
23
-
312Is the brain analog or digital?Cognitive Science Quarterly 1 (2): 147-170. 2000.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 true s…Read more
-
96Discreteness and relevance: A reply to Roman poznanski (review)Minds and Machines 12 (3): 437-438. 2002.
-
39Biologically Plausible, Human‐Scale Knowledge RepresentationCognitive Science 40 (4): 782-821. 2016.Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony, “mesh” binding, and conjunctive binding. Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate th…Read more
-
179A Neural Model of Rule Generation in Inductive ReasoningTopics in Cognitive Science 3 (1): 140-153. 2011.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 mod…Read more
-
115There 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 u…Read more
-
295How to build a brain: From function to implementationSynthese 153 (3): 373-388. 2006.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 to addressing both…Read more