David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Synthese 153 (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 questions. Specifically, to address 2, I adopt the Neural Engineering Framework (NEF) of Eliasmith & Anderson [Neural engineering: Computation representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press, 2003] which identifies implementational principles for neural models. To address 1, I suggest that adopting statistical modeling methods for perception and action will be functionally sufficient for capturing biological behavior. I show how these two answers will be mutually constraining, since the process of model selection for the statistical method in this approach can be informed by known anatomical and physiological properties of the brain, captured by the NEF. Similarly, the application of the NEF must be informed by functional hypotheses, captured by the statistical modeling approach
|Keywords||Neural architecture Functional integration Neurophilosophy Cognitive architecture Statistical models Mental representation Neural networks|
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References found in this work BETA
Allen Newell (1990). Unified Theories of Cognition. Harvard University Press.
William Bechtel & Robert C. Richardson (1993). Discovering Complexity Decomposition and Localization as Strategies in Scientific Research. Princeton.
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.
Rahul Sarpeshkar (1998). Analog Versus Digital: Extrapolating From Electronics to Neurobiology. Neural Computation 10 (7):1601--1638.
Citations of this work BETA
Gualtiero Piccinini & Sonya Bahar (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science 37 (3):453-488.
Andy Clark (2015). Radical Predictive Processing. Southern Journal of Philosophy 53:3-27.
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