How to build a brain: From function to implementation

Synthese 153 (3):373-388 (2007)
Abstract
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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DOI 10.1007/s11229-007-9235-0
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Unified Theories of Cognition.Allen Newell - 1990 - Harvard University Press.

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Radical Predictive Processing.Andy Clark - 2015 - Southern Journal of Philosophy 53 (S1):3-27.

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