Symbols, strings, and spikes
|Abstract||I argue that neural activity, strictly speaking, is not computation. This is because computation, strictly speaking, is the processing of strings of symbols, and neuroscience shows that there are no neural strings of symbols. This has two consequences. On the one hand, the following widely held consequences of computationalism must either be abandoned or supported on grounds independent of computationalism: (i) that in principle we can capture what is functionally relevant to neural processes in terms of some formalism taken from computability theory (such as Turing Machines), (ii) that it is possible to design computer programs that are functionally equivalent to neural processes in the same sense in which it is possible to design computer programs that are functionally equivalent to each other, (iii) that the study of neural (or mental) computation is independent of the study of neural implementation, (iv) that the Church-Turing thesis applies to neural activity in the sense in which it applies to digital computers. On the other hand, we need to gradually reinterpret or replace computational theories in psychology in terms of theoretical constructs that can be realized by known neural processes, such as the spike trains of neuronal ensembles.|
|Keywords||No keywords specified (fix it)|
|External links||This entry has no external links. Add one.|
|Through your library||Configure|
Similar books and articles
Stan Franklin & Max Garzon (1992). On Stability and Solvability (or, When Does a Neural Network Solve a Problem?). Minds and Machines 2 (1).
Patricia S. Churchland & Terrence J. Sejnowski (1989). Neural Representation and Neural Computation. In L. Nadel (ed.), Neural Connections, Mental Computations. MIT Press.
Gualtiero Piccinini (2004). The First Computational Theory of Mind and Brain: A Close Look at McCulloch and Pitts' Logical Calculus of Ideas Immanent in Nervous Activity. Synthese 141 (2):175-215.
Stevan Harnad, Symbol Grounding is an Empirical Problem: Neural Nets Are Just a Candidate Component.
Hava T. Siegelmann (2003). Neural and Super-Turing Computing. Minds and Machines 13 (1):103-114.
Drew McDermott (2001). The Digital Computer as Red Herring. Psycoloquy 12 (54).
Gualtiero Piccinini (2008). Some Neural Networks Compute, Others Don't. Neural Networks 21 (2-3):311-321.
Gualtiero Piccinini (2010). The Resilience of Computationalism. Philosophy of Science 77 (5):852-861.
Added to index2009-01-28
Total downloads25 ( #49,632 of 549,122 )
Recent downloads (6 months)3 ( #25,740 of 549,122 )
How can I increase my downloads?