Representation in Neuroscience Edited by Pete Mandik (William Paterson University)

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  • Kathleen Akins (1996). Of Sensory Systems and the "Aboutness" of Mental States. Journal of Philosophy 93 (7).
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  • Harald Atmanspacher, Interpreting Neurodynamics: Concepts and Facts.
    The dynamics of neuronal systems, briefly neurodynamics, has developed into an attractive and influential research branch within neuroscience. In this paper, we discuss a number of conceptual issues in neurodynamics that are important for an appropriate interpretation and evaluation of its results. We demonstrate their relevance for selected topics of theoretical and empirical work. In particular, we refer to the notions of determinacy and stochasticity in neurodynamics across levels of microscopic, mesoscopic and macroscopic descriptions. The issue of correlations between neural, (...)
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  • Harald Atmanspacher, The Significance of Causally Coupled, Stable Neuronal Assemblies for the Psychological Time Arrow.
    Stable neuronal assemblies are generally regarded as neural correlates of mental representations. Their temporal sequence corresponds to the experience of a direction of time, sometimes called the psychological time arrow. We show that the stability of particular, biophysically motivated models of neuronal assemblies, called coupled map lattices, is supported by causal interactions among neurons and obstructed by non-causal or anti-causal interactions among neurons. This surprising relation between causality and stability suggests that those neuronal assemblies that are stable due to causal (...)
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  • I. C. Baianu, R. Brown, G. Georgescu & J. F. Glazebrook (2006). Complex Non-Linear Biodynamics in Categories, Higher Dimensional Algebra and Łukasiewicz–Moisil Topos: Transformations of Neuronal, Genetic and Neoplastic Networks. Axiomathes 16 (1-2).
    A categorical, higher dimensional algebra and generalized topos framework for Łukasiewicz–Moisil Algebraic–Logic models of non-linear dynamics in complex functional genomes and cell interactomes is proposed. Łukasiewicz–Moisil Algebraic–Logic models of neural, genetic and neoplastic cell networks, as well as signaling pathways in cells are formulated in terms of non-linear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable ‘next-state functions’ is extended to a Łukasiewicz–Moisil (...)
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  • Richard Brown (2006). What is a Brain State? Philosophical Psychology 19 (6):729-742.
    Philosophers have been talking about brain states for almost 50 years and as of yet no one has articulated a theoretical account of what one is. In fact this issue has received almost no attention and cognitive scientists still use meaningless phrases like 'C-fiber firing' and 'neuronal activity' when theorizing about the relation of the mind to the brain. To date when theorists do discuss brain states they usually do so in the context of making some other argument with the (...)
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  • Massimiliano Cappuccio (2009). Constructing the Space of Action: From Bio-Robotics to Mirror Neurons. World Futures 65 (2):126 – 132.
    This article distinguishes three archetypal ways of articulating spatial cognition: (1) via metric representation of objective geometry, (2) via somatosensory constitution of the peripersonal environment, and (3) via pragmatic comprehension of the finalistic sense of action. The last one is documented by neuroscientific studies concerning mirror neurons. Bio-robotic experiments implementing mirror functions confirm the constitutive role of goal-oriented actions in spatial processes.
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  • Patricia S. Churchland & Terrence J. Sejnowski (1989). Neural Representation and Neural Computation. In L. Nadel (ed.), Neural Connections, Mental Computations. MIT Press.
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  • Paul M. Churchland (1986). Cognitive Neurobiology: A Computational Hypothesis for Laminar Cortex. Biology and Philosophy 1 (1):25-51.
    This paper outlines the functional capacities of a novel scheme for cognitive representation and computation, and it explores the possible implementation of this scheme in the massively parallel organization of the empirical brain. The suggestion is that the brain represents reality by means of positions in suitably constitutes phase spaces; and the brain performs computations on these representations by means of coordinate transformations from one phase space to another. This scheme may be implemented in the brain in two distinct forms: (...)
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  • Mike Collins (2009). The Nature and Implementation of Representation in Biological Systems. Dissertation, City University of New York
    I defend a theory of mental representation that satisfies naturalistic constraints. Briefly, we begin by distinguishing (i) what makes something a representation from (ii) given that a thing is a representation, what determines what it represents. Representations are states of biological organisms, so we should expect a unified theoretical framework for explaining both what it is to be a representation as well as what it is to be a heart or a kidney. I follow Millikan in explaining (i) in terms (...)
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  • Jeff Coulter (1995). The Informed Neuron: Issues in the Use of Information Theory in the Behavioral Sciences. Minds and Machines 5 (4):583-96.
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  • 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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  • Walter J. Freeman (1997). Nonlinear Neurodynamics of Intentionality. Journal of Mind and Behavior 18 (2-3):291-304.
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  • Angela D. Friederici & D. Yves von Cramon (2000). Syntax in the Brain: Linguistic Versus Neuroanatomical Specificity. Behavioral and Brain Sciences 23 (1):32-33.
    We criticize the lack of neuroanatomical precision in the Grodzinsky target article. We propose a more precise neuroanatomical characterization of syntactic processing and suggest that syntactic procedures are supported by the left frontal operculum in addition to the anterior part of the superior temporal gyrus, which appears to be associated with syntactic knowledge representation.
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  • James W. Garson (2003). The Introduction of Information Into Neurobiology. Philosophy of Science 70 (5):926-936.
    The first use of the term “information” to describe the content of nervous impulse occurs in Edgar Adrian's The Basis of Sensation (1928). What concept of information does Adrian appeal to, and how can it be situated in relation to contemporary philosophical accounts of the notion of information in biology? The answer requires an explication of Adrian's use and an evaluation of its situation in relation to contemporary accounts of semantic information. I suggest that Adrian's concept of information can be (...)
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  • Justin Garson, The Introduction of Information Into Neurobiology.
    The first use of the term "information" to describe the content of nervous impulse occurs 20 years prior to Shannon`s (1948) work, in Edgar Adrian`s The Basis of Sensation (1928). Although, at least throughout the 1920s and early 30s, the term "information" does not appear in Adrian`s scientific writings to describe the content of nervous impulse, the notion that the structure of nervous impulse constitutes a type of message subject to certain constraints plays an important role in all of his (...)
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  • Rick Grush (2003). In Defense of Some "Cartesian" Assumption Concerning the Brain and its Operation. Biology and Philosophy 18 (1):53-92.
    I argue against a growing radical trend in current theoretical cognitive science that moves from the premises of embedded cognition, embodied cognition, dynamical systems theory and/or situated robotics to conclusions either to the effect that the mind is not in the brain or that cognition does not require representation, or both. I unearth the considerations at the foundation of this view: Haugeland's bandwidth-component argument to the effect that the brain is not a component in cognitive activity, and arguments inspired by (...)
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  • Rick Grush (2001). The Semantic Challenge to Computational Neuroscience. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press.
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  • Carsten Held, Markus Knauff & Gottfried Vosgerau (eds.) (2006). Mental Models and the Mind: Current Developments in Cognitive Psychology, Neuroscience, and Philosophy of Mind. Elsevier.
    "Cognitive psychology," "cognitive neuroscience," and "philosophy of mind" are names for three very different scientific fields, but they label aspects of the same scientific goal: to understand the nature of mental phenomena. Today, the three disciplines strongly overlap under the roof of the cognitive sciences. The book's purpose is to present views from the different disciplines on one of the central theories in cognitive science: the theory of mental models. Cognitive psychologists report their research on the representation and processing of (...)
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  • Anne Jaap Jacobson (2003). Mental Representations: What Philosophy Leaves Out and Neuroscience Puts In. Philosophical Psychology 16 (2):189-204.
    This paper investigates how "representation" is actually used in some areas in cognitive neuroscience. It is argued that recent philosophy has largely ignored an important kind of representation that differs in interesting ways from the representations that are standardly recognized in philosophy of mind. This overlooked kind of representation does not represent by having intentional contents; rather members of the kind represent by displaying or instantiating features. The investigation is not simply an ethnographic study of the discourse of neuroscientists. If (...)
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  • Brian L. Keeley (1999). Fixing Content and Function in Neurobiological Systems: The Neuroethology of Electroreception. Biology and Philosophy 14 (3):395-430.
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  • Robert W. Kentridge (1995). Symbols, Neurons, Soap-Bubbles and the Neural Computation Underlying Cognition. Minds and Machines 4 (4).
    A wide range of systems appear to perform computation: what common features do they share? I consider three examples, a digital computer, a neural network and an analogue route finding system based on soap-bubbles. The common feature of these systems is that they have autonomous dynamics — their states will change over time without additional external influence. We can take advantage of these dynamics if we understand them well enough to map a problem we want to solve onto them. Programming (...)
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  • Pete Mandik (2005). Action-Oriented Representation. In Andrew Brook & Kathleen Akins (eds.), Cognition and the Brain: The Philosophy and Neuroscience Movement. Cambridge University Press.
    Often, sensory input underdetermines perception. One such example is the perception of illusory contours. In illusory contour perception, the content of the percept includes the presence of a contour that is absent from the informational content of the sensation. (By “sensation” I mean merely information-bearing events at the transducer level. I intend no further commitment such as the identification of sensations with qualia.) I call instances of perception underdetermined by sensation “underdetermined perception.” The perception of illusory contours is just one (...)
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  • Pete Mandik (2003). Varieties of Representation in Evolved and Embodied Neural Networks. Biology and Philosophy 18 (1):95-130.
    In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and (...)
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  • William D. Ross (1998). Filling-in While Finding Out: Guiding Behavior by Representing Information. Behavioral and Brain Sciences 21 (6):770-771.
    Discriminating behavior depends on neural representations in which the sensory activity patterns guiding different responses are decorrelated from one another. Visual information can often be parsimoniously transformed into these behavioral bridge-locus representations within neuro-computational visuo-spatial maps. Isomorphic inverse-optical world representation is not the goal. Nevertheless, such useful transformations can involve neural filling-in. Such a subpersonal representation of information is consistent with personal-level vision theory.
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  • Dan Ryder (2004). SINBaD Neurosemantics: A Theory of Mental Representation. Mind and Language 19 (2):211-240.
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  • Dan Ryder & Oleg Favorov (2001). The New Associationism: A Neural Explanation of the Predictive Powers of the Cerebral Cortex. Brain and Mind 2 (2):161-194.
    The ability to predict is the most importantability of the brain. Somehow, the cortex isable to extract regularities from theenvironment and use those regularities as abasis for prediction. This is a most remarkableskill, considering that behaviourallysignificant environmental regularities are noteasy to discern: they operate not only betweenpairs of simple environmental conditions, astraditional associationism has assumed, butamong complex functions of conditions that areorders of complexity removed from raw sensoryinputs. We propose that the brain's basicmechanism for discovering such complexregularities is implemented in (...)
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  • Alex Vereschagin, Mike Collins & Pete Mandik (2007). Evolving Artificial Minds and Brains. In Drew Khlentzos & Andrea Schalley (eds.), Mental States Volume 1: Evolution, function, nature. John Benjamins.
    We explicate representational content by addressing how representations that ex- plain intelligent behavior might be acquired through processes of Darwinian evo- lution. We present the results of computer simulations of evolved neural network controllers and discuss the similarity of the simulations to real-world examples of neural network control of animal behavior. We argue that focusing on the simplest cases of evolved intelligent behavior, in both simulated and real organisms, reveals that evolved representations must carry information about the creature’s environ- ments (...)
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