David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
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.
Allen Newell (1990). Unified Theories of Cognition. Harvard University Press.
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.
Similar books and articles
Christoph von der Malsburg (1995). Binding in Models of Perception and Brain Function. Current Opinion in Neurobiology 5:520-28.
R. Bauer (2004). In Search of a Neural Signature of Consciousness: Facts, Hypotheses, and Proposals. Synthese 141 (2):233-45.
Damian Keil & Keith Davids (2000). Lifting the Screen on Neural Organization: Is Computational Functional Modeling Necessary? Behavioral and Brain Sciences 23 (4):544-545.
Steven E. Petersen & Adina L. Roskies (2001). Visualizing Human Brain Function. In E. Bizzi, P. Calissano & V. Volterra (eds.), Frontiers of Life, Vol Iii: The Intelligent Systems, Part One: The Brain of Homo Sapiens. Academic Press.
Peter F. Dominey (2000). A Moveable Feast. Behavioral and Brain Sciences 23 (4):537-538.
Chris Eliasmith (forthcoming). Computational Neuroscience. In Paul R. Thagard (ed.), Philosophy of Psychology and Cognitive Science. Elsevier.
Martijn Meeter, Janneke Jehee & Jaap Murre (2007). Neural Models That Convince: Model Hierarchies and Other Strategies to Bridge the Gap Between Behavior and the Brain. Philosophical Psychology 20 (6):749 – 772.
Michael A. Arbib & Peter Érdi (2000). Organizing the Brain's Diversities. Behavioral and Brain Sciences 23 (4):551-565.
Michael A. Arbib & Péter Érdi (2000). Précis of Neural Organization: Structure, Function, and Dynamics. Behavioral and Brain Sciences 23 (4):513-533.
Peter Gouras (2000). Analyzing the Brain. Behavioral and Brain Sciences 23 (4):540-541.
Added to index2009-01-28
Total downloads67 ( #26,604 of 1,410,041 )
Recent downloads (6 months)16 ( #14,182 of 1,410,041 )
How can I increase my downloads?