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6.5a. Computation and Physical Systems (Computation and Physical Systems on PhilPapers)

- Computation and Physical Systems, Misc [1]
- Analog and Digital Computation [8]
- Computers [1]
- Implementing Computations [3]
- Noncomputable Processes [0]
- Pancomputationalism [5]
- Quantum Computation [3]

- Michael A. Bishop (2002). Counterfactuals cannot count: A rejoinder to David Chalmers. (More)
Additional links for this entry:

http://www.cirg.reading.ac.uk/common/publications/01245.pdf

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=12470628&dopt=Citation

http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&uid=12470628&cmd=showdetailview&indexed=google

http://linkinghub.elsevier.com/retrieve/pii/S1053810002000235

http://www.ingentaconnect.com/content/ap/cc/2002/00000011/00000004/art00023 - Andrew Boucher (1997). Parallel machines. (Abstract & more)Abstract: Because it is time-dependent, parallel computation is fundamentally different from sequential computation. Parallel programs are non-deterministic and are not effective procedures. Given the brain operates in parallel, this casts doubt on AI's attempt to make sequential computers intelligentAdditional links for this entry:

http://www.andrewboucher.com/papers/parallel.htm

http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=316240CI

http://www.springerlink.com/content/content/m21w0q0p16486455/fulltext.pdf

http://www.springerlink.com/content/m21w0q0p16486455/fulltext.pdf

http://www.kluweronline.com/article.asp?PIPS=134098&PDF=1

http://www.springerlink.com/index/M21W0Q0P16486455.pdf

http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000004/00134098 - Curtis Brown (2004). Implementation and indeterminacy. (Abstract & more)Abstract: David Chalmers has defended an account of what it is for a physical system to implement a computation. The account appeals to the idea of a “combinatorial-state automaton” or CSA. It is unclear whether Chalmers intends the CSA to be a computational model in the usual sense, or merely a convenient formalism into which instances of other models can be translated. I argue that the CSA is not a computational model in the usual sense because CSAs do not perspicuously represent algorithms, are too powerful both in that they can perform any computation in a single step and in that without so far unspecified restrictions they can “compute” the uncomputable, and are too loosely related to physical implementationsAdditional links for this entry:

http://portal.acm.org/citation.cfm?id=1082145.1082150

http://www.trinity.edu/cbrown/papers/implementation.pdf - Paul Bohan Broderick (2004). On communication and computation. (Abstract & more)Abstract: Comparing technical notions of communication and computation leads to a surprising result, these notions are often not conceptually distinguishable. This paper will show how the two notions may fail to be clearly distinguished from each other. The most famous models of computation and communication, Turing Machines and (Shannon-style) information sources, are considered. The most significant difference lies in the types of state-transitions allowed in each sort of model. This difference does not correspond to the difference that would be expected after considering the ordinary usage of these terms. However, the natural usage of these terms are surprisingly difficult to distinguish from each other. The two notions may be kept distinct if computation is limited to actions within a system and communications is an interaction between a system and its environment. Unfortunately, this decision requires giving up much of the nuance associated with natural language versions of these important terms
- David J. Chalmers (1996). Does a rock implement every finite-state automaton? (Abstract & more)Abstract: Hilary Putnam has argued that computational functionalism cannot serve as a foundation for the study of the mind, as every ordinary open physical system implements every finite-state automaton. I argue that Putnam's argument fails, but that it points out the need for a better understanding of the bridge between the theory of computation and the theory of physical systems: the relation of implementation. It also raises questions about the class of automata that can serve as a basis for understanding the mind. I develop an account of implementation, linked to an appropriate class of automata, such that the requirement that a system implement a given automaton places a very strong constraint on the system. This clears the way for computation to play a central role in the analysis of mindAdditional links for this entry:

http://consc.net/papers/rock.html

http://citeseer.ist.psu.edu/72482.html

http://citeseer.ist.psu.edu/chalmers96does.html

http://www.u.arizona.edu/~chalmers/papers/rock.html

http://cogprints.soton.ac.uk/documents/disk0/00/00/02/26/

http://cogprints.ecs.soton.ac.uk/archive/00000226/00/199708001.html

http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:226

http://www.springerlink.com/content/ql4047230q675124/fulltext.pdf

http://www.springerlink.com/index/QL4047230Q675124.pdf

http://cogprints.org/226/1/199708001.html

http://cogprints.org/226/0/199708001.html - Carol E. Cleland (2001). Recipes, algorithms, and programs. (Abstract & more)Abstract: In the technical literature of computer science, the concept of an effective procedure is closely associated with the notion of an instruction that precisely specifies an action. Turing machine instructions are held up as providing paragons of instructions that "precisely describe" or "well define" the actions they prescribe. Numerical algorithms and computer programs are judged effective just insofar as they are thought to be translatable into Turing machine programs. Nontechnical procedures (e.g., recipes, methods) are summarily dismissed as ineffective on the grounds that their instructions lack the requisite precision. But despite the pivotal role played by the notion of a precisely specified instruction in classifying procedures as effective and ineffective, little attention has been paid to the manner in which instructions "precisely specify" the actions they prescribe. It is the purpose of this paper to remedy this defect. The results are startling. The reputed exemplary precision of Turing machine instructions turns out to be a myth. Indeed, the most precise specifications of action are provided not by the procedures of theoretical computer science and mathematics (algorithms) but rather by the nontechnical procedures of everyday life. I close with a discussion of some of the rumifications of these conclusions for understanding and designing concrete computers and their programming languagesAdditional links for this entry:

http://www.cse.buffalo.edu/~rapaport/Papers/Papers.by.Others/mm.cleland.pdf

http://www.springerlink.com/content/content/lr1t0l5ln4232384/fulltext.pdf

http://www.springerlink.com/content/lr1t0l5ln4232384/fulltext.pdf

http://www.kluweronline.com/article.asp?PIPS=323321&PDF=1

http://www.springerlink.com/index/LR1T0L5LN4232384.pdf

http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000002/00323321 - Cristian Cocos (2002). Computational processes: A reply to Chalmers and Copeland. (More)
- Jack Copeland (1999). Beyond the universal Turing machine. (Abstract & more)Abstract: We describe an emerging field, that of nonclassical computability and nonclassical computing machinery. According to the nonclassicist, the set of well-defined computations is not exhausted by the computations that can be carried out by a Turing machine. We provide an overview of the field and a philosophical defence of its foundationsAdditional links for this entry:

http://citeseer.ist.psu.edu/copeland98beyond.html

http://www.eur.nl/fw/staff/lokhorst/jack/beyond.pdf

http://www.alanturing.net/turing_archive/pages/pub/beyond/beyond.pdf

http://www.informaworld.com/smpp/./ftinterface~content=a739205973~fulltext=713240930

http://taylorandfrancis.metapress.com/index/V8X4457533598348.pdf

http://www.informaworld.com/index/V8X4457533598348.pdf

http://www.ingentaconnect.com/content/routledg/ajphil/1999/00000077/00000001/art00003

http://www.ingentaconnect.com/content/tandf/tajp/1999/00000077/00000001/art00003

http://www.informaworld.com/smpp/./ftinterface~db=all~content=a739205973~fulltext=713240930 - Jack Copeland (1998). Super Turing-machines. (Abstract & more)Abstract: The tape is divided into squares, each square bearing a single symbol—'0' or '1', for example. This tape is the machine's general-purpose storage medium: the machine is set in motion with its input inscribed on the tape, output is written onto the tape by the head, and the tape serves as a short-term working memory for the results of intermediate steps of the computation. The program governing the particular computation that the machine is to perform is also stored on the tape. A small, fixed program that is 'hard-wired' into the head enables the head to read and execute the instructions of whatever program is on the tape. The machine's atomic operations are very simple—for example, 'move left one square', 'move right one square', 'identify the symbol currently beneath the head', 'write 1 on the square that is beneath the head', and 'write 0 on the square that is beneath the head'. Complexity of operation is achieved by the chaining together of large numbers of these simple atoms. Any universal Turing machine can be programmed to carry out any calculation that can be performed by a human mathematician working with paper and pencil in accordance with some algorithmic method. This is what is meant by calling these machines 'universal'Additional links for this entry:

http://www.eur.nl/fw/staff/lokhorst/jack/super.pdf

http://portal.acm.org/citation.cfm?id=303264.303271

http://www.alanturing.net/turing_archive/pages/pub/super/super.pdf

http://doi.wiley.com/10.1002/(SICI)1099-0526(199809/10)4:1<30::AID-CPLX9>3.3.CO;2-#

http://doi.wiley.com/10.1002/(SICI)1099-0526(199809/10)4:1<30::AID-CPLX9>3.0.CO;2-8 - Jack Copeland (1997). The broad conception of computation. (Abstract & more)Abstract: A myth has arisen concerning Turing's paper of 1936, namely that Turing set forth a fundamental principle concerning the limits of what can be computed by machine - a myth that has passed into cognitive science and the philosophy of mind, to wide and pernicious effect. This supposed principle, sometimes incorrectly termed the 'Church-Turing thesis', is the claim that the class of functions that can be computed by machines is identical to the class of functions that can be computed by Turing machines. In point of fact Turing himself nowhere endorses, nor even states, this claim (nor does Church). I describe a number of notional machines, both analogue and digital, that can compute more than a universal Turing machine. These machines are exemplars of the class of _nonclassical_ computing machines. Nothing known at present rules out the possibility that machines in this class will one day be built, nor that the brain itself is such a machine. These theoretical considerations undercut a number of foundational arguments that are commonly rehearsed in cognitive science, and gesture towards a new class of cognitive models
- B. Jack Copeland (1996). What is computation? (Abstract & more)Abstract: To compute is to execute an algorithm. More precisely, to say that a device or organ computes is to say that there exists a modelling relationship of a certain kind between it and a formal specification of an algorithm and supporting architecture. The key issue is to delimit the phrase of a certain kind. I call this the problem of distinguishing between standard and nonstandard models of computation. The successful drawing of this distinction guards Turing's 1936 analysis of computation against a difficulty that has persistently been raised against it, and undercuts various objections that have been made to the computational theory of mindAdditional links for this entry:

http://www.springerlink.com/content/u005510gxl27q850/fulltext.pdf

http://www.springerlink.com/index/U005510GXL27Q850.pdf - Vinod Goel (1991). Notationality and the information processing mind. (Abstract & more)Abstract: Cognitive science uses the notion of computational information processing to explain cognitive information processing. Some philosophers have argued that anything can be described as doing computational information processing; if so, it is a vacuous notion for explanatory purposes.An attempt is made to explicate the notions of cognitive information processing and computational information processing and to specify the relationship between them. It is demonstrated that the resulting notion of computational information processing can only be realized in a restrictive class of dynamical systems called physical notational systems (after Goodman's theory of notationality), and that the systems generally appealed to by cognitive science-physical symbol systems-are indeed such systems. Furthermore, it turns out that other alternative conceptions of computational information processing, Fodor's (1975) Language of Thought and Cummins' (1989) Interpretational Semantics appeal to substantially the same restrictive class of systemsAdditional links for this entry:

http://cogprints.ecs.soton.ac.uk/archive/00000693/

http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:693

http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:693

http://www.springerlink.com/content/r617254537x77248/fulltext.pdf

http://www.springerlink.com/index/R617254537X77248.pdf - Roman Stanisław Ingarden (2002). Open systems and consciousness: A philosophical discussion. (More)
Additional links for this entry:

http://portal.acm.org/citation.cfm?id=598889.599024

http://www.springerlink.com/index/R760651G11L0V568.pdf

http://www.springerlink.com/index/74PFWEA53N77J4QY.pdf

http://www.ingentaconnect.com/content/klu/opsy/2002/00000009/00000004/05115613

http://www.ingentaconnect.com/content/klu/opsy/2002/00000009/00000002/05091540 - Colin Klein (web). Dispositional implementation solves the superfluous structure problem. (Abstract & more)Abstract: Consciousness supervenes on activity; computation supervenes on structure. Because of this, some argue, conscious states cannot supervene on computational ones. If true, this would present serious di?culties for computationalist analyses of consciousness (or, indeed, of any domain with properties that supervene on actual activity). I argue that the computationalist can avoid the Super?uous Structure Problem by moving to a dispositional theory of implementation. On a dispositional theory, the activity of computation depends entirely on changes in the intrinsic properties of implementing material. As extraneous structure is not required for computation, a system can implement a program running on some but not all possible inputs. Dispositional computationalism thus permits episodes of computational activity that correspond to potential episodes of conscious awareness. The Super?uous Structure Problem cannot be motivated against this account, and so computationalism may be preservedAdditional links for this entry:

http://www.springerlink.com/content/968m58t775k26514/fulltext.pdf

http://www.springerlink.com/index/968m58t775k26514.pdf - Bruce J. MacLennan (1993). Grounding analog computers. (Abstract & more)Abstract: In this commentary on Harnad's "Grounding Symbols in the Analog World with Neural Nets: A Hybrid Model," the issues of symbol grounding and analog (continuous) computation are separated, it is argued that symbol graounding is as important an issue for analog cognitive models as for digital (discrete) models. The similarities and differences between continuous and discrete computation are discussed, as well as the grounding of continuous representations. A continuous analog of the Chinese Room is presentedAdditional links for this entry:

http://cogprints.org/542/01/GAC.ps

http://citeseer.ist.psu.edu/181625.html

http://cogprints.ecs.soton.ac.uk/archive/00000542/

http://psycprints.ecs.soton.ac.uk/archive/00000181/

http://cogsci.soton.ac.uk/~harnad/Temp/Think/maclenn.htm

http://citebase.eprints.org/cgi-bin/citations?id=oai:cogprints.soton.ac.uk:542

http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:542

http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.maclennan.html

http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.maclennan.html

http://cogprints.org/542/1/GAC.ps

http://cogprints.org/542/0/GAC.ps - B. Maclennan (2003). Transcending Turing computability. (Abstract & more)Abstract: It has been argued that neural networks and other forms of analog computation may transcend the limits of Turing-machine computation; proofs have been offered on both sides, subject to differing assumptions. In this article I argue that the important comparisons between the two models of computation are not so much mathematical as epistemological. The Turing-machine model makes assumptions about information representation and processing that are badly matched to the realities of natural computation (information representation and processing in or inspired by natural systems). This points to the need for new models of computation addressing issues orthogonal to those that have occupied the traditional theory of computationAdditional links for this entry:

http://citeseer.ist.psu.edu/490560.html

http://portal.acm.org/citation.cfm?id=607932

http://www.cs.utk.edu/~mclennan/anon-ftp/TTC.ps

http://citeseer.ist.psu.edu/maclennan01transcending.html

http://www.cs.utk.edu/~library/TechReports/2001/ut-cs-01-473.ps

http://www.cs.queensu.ca/home/akl/cisc879/papers/PAPERS_FROM_MINDS_AND_MACHINES/VOLUME_13_NO_1/V1846164611805820.pdf

http://www.springerlink.com/content/v184616461805820/fulltext.pdf

http://www.kluweronline.com/article.asp?PIPS=5098146&PDF=1

http://www.springerlink.com/index/V184616461805820.pdf

http://www.ingentaconnect.com/content/klu/mind/2003/00000013/00000001/05098146 - Jacques Mallah (ms). The many computations interpretation (MCI) of quantum mechanics. (Abstract & more)Abstract: Computationalism provides a framework for understanding how a mathematically describable physical world could give rise to conscious observations without the need for dualism. A criterion is proposed for the implementation of computations by physical systems, which has been a problem for computationalism. Together with an independence criterion for implementations this would allow, in principle, prediction of probabilities for various observations based on counting implementations. Applied to quantum mechanics, this results in a Many Computations Interpretation (MCI), which is an explicit form of the Everett style Many Worlds Interpretation (MWI). Derivation of the Born Rule emerges as the central problem for most realist interpretations of quantum mechanics. If the Born Rule is derived based on computationalism and the wavefunction it would provide strong support for the MWI; but if the Born Rule is shown not to follow from these to an experimentally falsified extent, it would indicate the necessity for either new physics or (more radically) new philosophy of mind.
- Marcin Miłkowski (2009). Is Evolution Algorithmic? (Abstract & more)Abstract: In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical processes. For that to be true, the processes have to be part of computational systems. The basic difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements this poses for natural evolutionary processes if they are to be computational.
- Marcin Miłkowski (2007). Is computationalism trivial? (Abstract & more)Abstract: In this paper, I want to deal with the triviality threat to computationalism. On one hand, the controversial and vague claim that cognition involves computation is still denied. On the other, contemporary physicists and philosophers alike claim that all physical processes are indeed computational or algorithmic. This claim would justify the computationalism claim by making it utterly trivial. I will show that even if these two claims were true, computationalism would not have to be trivial.
- Gualtiero Piccinini (2007). Computing mechanisms. (Abstract & more)Abstract: This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power.Additional links for this entry:

http://www.journals.uchicago.edu/doi/full/10.1086/522851

http://www.journals.uchicago.edu/doi/abs/10.1086/522851 - Gualtiero Piccinini (2008). Computation without representation. (Abstract & more)Abstract: The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational states are individuated within computability theory and computer science. A secondary purpose is to show that existing arguments for the semantic view are defective.
- Paul Schweizer (2002). Consciousness and computation. (More)
Additional links for this entry:

http://www.springerlink.com/content/w12757pt85060n27/fulltext.pdf

http://www.springerlink.com/index/W12757PT85060N27.pdf - Matthias Scheutz (1999). When physical systems realize functions. (Abstract & more)Abstract: After briefly discussing the relevance of the notions computation and implementation for cognitive science, I summarize some of the problems that have been found in their most common interpretations. In particular, I argue that standard notions of computation together with a state-to-state correspondence view of implementation cannot overcome difficulties posed by Putnam's Realization Theorem and that, therefore, a different approach to implementation is required. The notion realization of a function, developed out of physical theories, is then introduced as a replacement for the notional pair computation-implementation. After gradual refinement, taking practical constraints into account, this notion gives rise to the notion digital system which singles out physical systems that could be actually used, and possibly even builtAdditional links for this entry:

http://portal.acm.org/citation.cfm?id=596816

http://citeseer.ist.psu.edu/scheutz99when.html

http://www.springerlink.com/content/n624620t45402053/fulltext.pdf

http://www.kluweronline.com/article.asp?PIPS=206284&PDF=1

http://www.springerlink.com/index/N624620T45402053.pdf

http://www.ingentaconnect.com/content/klu/mind/1999/00000009/00000002/00206284 - John R. Searle (1990). Is the brain a digital computer? (Abstract & more)Abstract: There are different ways to present a Presidential Address to the APA; the one I have chosen is simply to report on work that I am doing right now, on work in progress. I am going to present some of my further explorations into the computational model of the mind.\**Additional links for this entry:

http://citeseer.ist.psu.edu/searle04is.html

http://cogsci.soton.ac.uk/~harnad/Papers/Py104/searle.comp.html

http://www.ecs.soton.ac.uk/~harnad/Papers/Py104/searle.comp.html

http://philosophy.wisc.edu/shapiro/Phil554/PAPERS/Is the Brain a Digital Computer.htm

http://www.engadin.nl/bibliotheek/bibliotheek/download_files/Searle BrainDigitalcomputer.pdf

http://links.jstor.org/sici?sici=0065-972X(199011)64:3<21:ITBADC>2.0.CO;2-8 - Oron Shagrir (1997). Two dogmas of computationalism. (Abstract & more)Abstract: This paper challenges two orthodox theses: (a) that computational processes must be algorithmic; and (b) that all computed functions must be Turing-computable. Section 2 advances the claim that the works in computability theory, including Turing's analysis of the effective computable functions, do not substantiate the two theses. It is then shown (Section 3) that we can describe a system that computes a number-theoretic function which is not Turing-computable. The argument against the first thesis proceeds in two stages. It is first shown (Section 4) that whether a process is algorithmic depends on the way we describe the process. It is then argued (Section 5) that systems compute even if their processes are not described as algorithmic. The paper concludes with a suggestion for a semantic approach to computation
- Aaron Sloman (ms). Supervenience and implementation. (Abstract & more)Abstract: How can a virtual machine X be implemented in a physical machine Y? We know the answer as far as compilers, editors, theorem-provers, operating systems are concerned, at least insofar as we know how to produce these implemented virtual machines, and no mysteries are involved. This paper is about extrapolating from that knowledge to the implementation of minds in brains. By linking the philosopher's concept of supervenience to the engineer's concept of implementation, we can illuminate both. In particular, by showing how virtual machines can be implemented in causally complete physical machines, and still have causal powers, we remove some philosophical problems about how mental processes can be real and can have real effects in the world even if the underlying physical implementation has no causal gaps. This requires a theory of ontological levelsAdditional links for this entry:

http://citeseer.ist.psu.edu/172002.html

http://cogprints.org/333/00/Sloman.supervenience.and.implementation.ps

http://www.cs.bham.ac.uk/research/cogaff/Sloman.supervenience.and.implementation.ps

http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:333

http://cogprints.ecs.soton.ac.uk/archive/00000333/00/Sloman.supervenience.and.implementation.ps

http://cogprints.org/333/2/Sloman.supervenience.and.implementation.ps

http://cogprints.org/333/0/Sloman.supervenience.and.implementation.ps - Aaron Sloman (online). What are virtual machines? Are they real? (More)
- Peter Suber (1988). What is software? (More)

Computation and Physical Systems, Misc

- Gualtiero Piccinini (2008). Some Neural Networks Compute, Others Don't. (Abstract & more)Abstract: I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend

the following theses. (1) Many neural networks compute—they perform computations. (2) Some neural networks compute in a classical way.

Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks

compute in a non-classical way. (4) Yet other neural networks do not perform computations. Brains may well fall into this last class.

Analog and Digital Computation

- David J. Chalmers (manuscript). Analog vs. digital computation. (Abstract & more)Abstract: It is fairly well-known that certain hard computational problems (that is, 'difficult' problems for a digital processor to solve) can in fact be solved much more easily with an analog machine. This raises questions about the true nature of the distinction between analog and digital computation (if such a distinction exists). I try to analyze the source of the observed difference in terms of (1) expanding parallelism and (2) more generally, infinite-state Turing machines. The issue of discreteness vs continuity will also be touched upon, although it is not so important for analyzing these particular problems
- Chris Eliasmith (2000). Is the brain analog or digital? (Abstract & more)Abstract: It will always remain a remarkable phenomenon in the history of philosophy, that there was a time, when even mathematicians, who at the same time were philosophers, began to doubt, not of the accuracy of their geometrical propositions so far as they concerned space, but of their objective validity and the applicability of this concept itself, and of all its corollaries, to nature. They showed much concern whether a line in nature might not consist of physical points, and consequently that true space in the object might consist of simple [discrete] parts, while the space which the geometer has in his mind [being continuous] cannot be suchAdditional links for this entry:

http://www.arts.uwaterloo.ca/~celiasmi/Papers/ce.2000.continuity.debate.csq.html - Bruce J. MacLennan (1994). Words lie in our way. (Abstract & more)Abstract: The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scientific claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog computation and digital computation are not essentially different, and so arguments such as Searle''s hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scientific matter of fact. (4) A substantive scientific question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it''s all too easy to become ensnaredAdditional links for this entry:

http://cogprints.ecs.soton.ac.uk/archive/00000383/00/WLIOW.ps

http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:383

http://www.springerlink.com/content/rg33148365240004/fulltext.pdf

http://www.springerlink.com/index/RG33148365240004.pdf

http://cogprints.org/383/1/WLIOW.ps

http://cogprints.org/383/0/WLIOW.ps - Corey J. Maley (forthcoming). Analog and digital, continuous and discrete. (Abstract & more)Abstract: Representation is central to contemporary theorizing about the mind/brain. But the nature of representation--both in the mind/brain and more generally--is a source of ongoing controversy. One way of categorizing representational types is to distinguish between the analog and the digital: the received view is that analog representations vary smoothly, while digital representations vary in a step-wise manner. I argue that this characterization is inadequate to account for the ways in which representation is used in cognitive science; in its place, I suggest an alternative taxonomy. I will defend and extend David Lewis's account of analog and digital representation, distinguishing analog from continuous representation, as well as digital from discrete representation. I will argue that the distinctions available in this four-fold account accord with representational features of theoretical interest in cognitive science more usefully than the received analog/digital dichotomy
- Drew McDermott (2001). The digital computer as red Herring. (Abstract & more)Abstract: Stevan Harnad correctly perceives a deep problem in computationalism, the hypothesis that cognition is computation, namely, that the symbols manipulated by a computational entity do not automatically mean anything. Perhaps, he proposes, transducers and neural nets will not have this problem. His analysis goes wrong from the start, because computationalism is not as rigid a set of theories as he thinks. Transducers and neural nets are just two kinds of computational system, among many, and any solution to the semantic problem that works for them will work for most other computational systemsAdditional links for this entry:

http://psycprints.ecs.soton.ac.uk/archive/00000183/

http://psycprints.ecs.soton.ac.uk/archive/00000183/

http://cogsci.soton.ac.uk/~harnad/Temp/Think/mcdermot.htm

http://psycprints.ecs.soton.ac.uk/perl/local/psyc/makedoc?id=183&type=html

http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.mcdermott.html

http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad93.symb.anal.net.mcdermott.html - Gualtiero Piccinini (2008). Computers. (Abstract & more)Abstract: I offer an explication of the notion of computer, grounded in the practices of computability theorists and computer scientists. I begin by explaining what distinguishes computers from calculators. Then, I offer a systematic taxonomy of kinds of computer, including hard-wired versus programmable, general-purpose versus special-purpose, analog versus digital, and serial versus parallel, giving explicit criteria for each kind. My account is mechanistic: which class a system belongs in, and which functions are computable by which system, depends on the system's mechanistic properties. Finally, I briefly illustrate how my account sheds light on some issues in the history and philosophy of computing as well as the philosophy of mind.
- Rahul Sarpeshkar (1998). Analog versus digital: Extrapolating from electronics to neurobiology. (Abstract & more)Abstract: We review the pros and cons of analog and digital computation. We propose that computation that is most efficient in its use of resources is neither analog computation nor digital computation but, rather, a mixture of the two forms. For maximum efficiency, the information and information-processing resources of the hybrid form must be distributed over many wires, with an optimal signal-to-noise ratio per wire. Our results suggest that it is likely that the brain computes in a hybrid fashion and that an underappreciated and important reason for the efficiency of the human brain, which consumes only 12 W, is the hybrid and distributed nature of its architecture.
- Hava T. Siegelmann (2003). Neural and super-Turing computing. (Abstract & more)Abstract: ``Neural computing'' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation'', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing that gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machineAdditional links for this entry:

http://www.cs.queensu.ca/home/akl/cisc879/papers/PAPERS_FROM_MINDS_AND_MACHINES/VOLUME_13_NO_1/J7L1675237505M16.pdf

http://www.springerlink.com/content/j7l1675237505m16/fulltext.pdf

http://www.kluweronline.com/article.asp?PIPS=5098701&PDF=1

http://journals.kluweronline.com/article.asp?PIPS=5098701

http://www.springerlink.com/index/J7L1675237505M16.pdf

http://www.ingentaconnect.com/content/klu/mind/2003/00000013/00000001/05098701

- Gualtiero Piccinini (2008). Some Neural Networks Compute, Others Don't. (Abstract & more)Abstract: I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend

the following theses. (1) Many neural networks compute—they perform computations. (2) Some neural networks compute in a classical way.

Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks

compute in a non-classical way. (4) Yet other neural networks do not perform computations. Brains may well fall into this last class.

- David J. Chalmers (ms). A computational foundation for the study of cognition. (Abstract & more)Abstract: Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that (a) computation can specify general patterns of causal organization, and (b) mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive scienceAdditional links for this entry:

http://cogprints.org/319/1/computation.html

http://cogprints.org/319/0/computation.html - Jacques Mallah (ms). The partial brain thought experiment: Partial consciousness and its implications. (Abstract & more)Abstract: The ‘Fading Qualia’ thought experiment of Chalmers purports to show that computationalism is very probably true even if dualism is true by considering a series of brains, with biological parts increasingly substituted for by artificial but functionally analagous parts in small steps, and arguing that consciousness would not plausibly vanish in either a gradual or sudden way. This defense of computationalism inspired an attack on computationalism by Bishop, who argued that a similar series of substitutions by parts that have the correct physical activity but not the correct causal relationships must likewise preserve consciousness, purportedly showing that ‘Counterfactuals Cannot Count’ and if so ruining a necessary condition for computation to meaningfully distinguish between physical systems. In this paper, the case in which a series of parts are simply removed and substituted for only by imposing the correct boundary conditions to exactly preserve the functioning of the remaining partial brain is described. It is argued that consciousness must gradually vanish in this case, not by fading but by becoming more and more partial. This supports the non-centralized nature of consciousness, tends to support the plausibility of physicalism against dualism, and provides the proper counterargument to Bishop’s contention. It also provides an avenue of attack against the “Fading Qualia” argument for those who remain dualists
- Yaroslav D. Sergeyev (2008). A new applied approach for executing computations with infinite and infinitesimal quantities. (Abstract & more)Abstract: A new computational methodology for executing calculations with infinite and infinitesimal quantities is described in this paper. It is based on the principle ‘The part is less than the whole’ introduced by Ancient Greeks and applied to all numbers (finite, infinite, and infinitesimal) and to all sets and processes (finite and infinite). It is shown that it becomes possible to write down finite, infinite, and infinitesimal numbers by a finite number of symbols as particular cases of a unique framework. The new methodology has allowed us to introduce the Infinity Computer working with such numbers (its simulator has already been realized). Examples dealing with divergent series, infinite sets, and limits are given.

- Gordana Dodig-Crnkovic (2008). Empirical Modeling and Information Semantics. (Abstract & more)Abstract: This paper investigates the relationship between reality and model, information and truth. It will argue that meaningful data need not be true in order to constitute information. Information to which truth-value cannot be ascribed, partially true information or even false information can lead to an interesting outcome such as technological innovation or scientific breakthrough. In the research process, during the transition between two theoretical frameworks, there is a dynamic mixture of old and new concepts in which truth is not well defined. Instead of veridicity, correctness of a model and its appropriateness within a context are commonly required. Despite empirical models being in general only truthlike, they are nevertheless capable of producing results from which conclusions can be drawn and adequate decisions made.
- Gordana Dodig-Crnkovic (2008). Knowledge Generation as Natural Computation. (Abstract & more)Abstract: Knowledge generation can be naturalized by adopting computational model of cognition and evolutionary approach. In this framework knowledge is seen as a result of the structuring of input data (data → information → knowledge) by an interactive computational process going on in the agent during the adaptive interplay with the environment, which clearly presents developmental advantage by increasing agent’s ability to cope with the situation dynamics. This paper addresses the mechanism of knowledge generation, a process that may be modeled as natural computation in order to be better understood and improved
- Gordana Dodig-Crnkovic (online). Semantics of Information as Interactive Computation. (Abstract & more)Abstract: Computers today are not only the calculation tools - they are directly (inter)acting in the physical world which itself may be conceived of as the universal computer (Zuse, Fredkin, Wolfram, Chaitin, Lloyd). In expanding its domains from abstract logical symbol manipulation to physical embedded and networked devices, computing goes beyond Church-Turing limit (Copeland, Siegelman, Burgin, Schachter). Computational processes are distributed, reactive, interactive, agent-based and concurrent. The main criterion of success of computation is not its termination, but the adequacy of its response, its speed, generality and flexibility; adaptability, and tolerance to noise, error,faults, and damage. Interactive computing is a generalization of Turing computing, and it calls for new conceptualizations (Goldin, Wegner). In the info-computationalist framework, with computation seen as information processing, natural computation appears as the most suitable paradigm of computation and information semantics requires logical pluralism.
- Rowan Grigg (ms). A case for lattice schemes in fundamental physics. (Abstract & more)Abstract: A synthesis of trending topics in pancomputationalism. I introduce the notion that "strange loops" engender the most atomic levels of physical reality, and introduce a mechanism for global non-locality. Writen in a simple and accesssible style, it seeks to draw research in fundamental physics back to realism, and have a bit of fun in the process.
- Rowan Grigg (ms). The Universal Lattice. (More)

- Rowan Grigg (ms). The Universal Lattice. (More)
- Stuart R. Hameroff (online). Consciousness, Whitehead and quantum computation in the brain: Panprotopsychism meets the physics of fundamental spacetime geometry. (Abstract & more)Abstract: _dualism_ (consciousness lies outside knowable science), _emergence_ (consciousness arises as a novel property from complex computational dynamics in the brain), and some form of _panpsychism_, _pan-protopsychism, or pan-experientialism_ (essential features or precursors of consciousness are fundamental components of reality which are accessed by brain processes). In addition to 1) the problem of subjective experience, other related enigmatic features of consciousness persist, defying technological and philosophical inroads. These include 2) the “binding problem”—how disparate brain activities give rise to a unified sense of “self” or unified conscious content. Temporal synchrony—brain-wide coherence of neural membrane electrical activities—is often assumed to accomplish binding, but _what_ is being synchronized? What is being coherently bound? Another enigmatic feature is 3) the transition from pre-conscious processes to consciousness itself. Most neuroscientists agree that consciousness is the “tip of an iceberg”, that the vast majority of brain activities is
- Stuart R. Hameroff (2002). Quantum computation in brain microtubules. (Abstract & more)Abstract: Proposals for quantum computation rely on superposed states implementing multiple computations simultaneously, in parallel, according to quantum linear superposition (e.g., Benioff, 1982; Feynman, 1986; Deutsch, 1985, Deutsch and Josza, 1992). In principle, quantum computation is capable of specific applications beyond the reach of classical computing (e.g., Shor, 1994). A number of technological systems aimed at realizing these proposals have been suggested and are being evaluated as possible substrates for quantum computers (e.g. trapped ions, electron spins, quantum dots, nuclear spins, etc., see Table 1; Bennett, 1995; and Barenco, 1995). The main obstacle to realization of quantum computation is the problem of interfacing to the system (input, output) while also protecting the quantum state from environmental decoherence. If this problem can be overcome, then present day classical computers may evolve to quantum computers

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