David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Biology and Philosophy 18 (1):95-130 (2003)
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 anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem
|Keywords||Brain Evolution Network Neural Representation Science|
|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
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
François Chapeau-Blondeau (1995). Information Processing in Neural Networks by Means of Controlled Dynamic Regimes. Acta Biotheoretica 43 (1-2):155-167.
Patricia S. Churchland & Terrence J. Sejnowski (1989). Neural Representation and Neural Computation. In L. Nadel (ed.), Philosophical Perspectives. MIT Press. 343-382.
Enrico Blanzieri (1997). Dynamical Learning Algorithms for Neural Networks and Neural Constructivism. Behavioral and Brain Sciences 20 (4):559-559.
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.
R. C. Lacher (1993). Expert Networks: Paradigmatic Conflict, Technological Rapproachement. [REVIEW] Minds and Machines 3 (1):53-71.
Paul A. Koch & Gerry Leisman (2004). The Local is Running on the Express Track: Localist Models Better Facilitate Understanding of Nervous System Function. Behavioral and Brain Sciences 27 (5):700-700.
Dan Hunter (1999). Out of Their Minds: Legal Theory in Neural Networks. [REVIEW] Artificial Intelligence and Law 7 (2-3):129-151.
Aarre Laakso & Garrison W. Cottrell (2000). Content and Cluster Analysis: Assessing Representational Similarity in Neural Systems. Philosophical Psychology 13 (1):47-76.
Stan Franklin & Max Garzon (1992). On Stability and Solvability (or, When Does a Neural Network Solve a Problem?). Minds and Machines 2 (1):71-83.
Gualtiero Piccinini (2008). Some Neural Networks Compute, Others Don't. Neural Networks 21 (2-3):311-321.
Added to index2009-01-28
Total downloads131 ( #9,483 of 1,692,888 )
Recent downloads (6 months)30 ( #5,498 of 1,692,888 )
How can I increase my downloads?