What is the significance of high-speed computation for the sciences? How far does it result in a practice of simulation which affects the sciences on a very basic level? To offer more historical context to these recurring questions, this paper revisits the roots of computer simulation in the development of the ENIAC computer and the Monte Carlo method.With the aim of identifying more clearly what really changed (or not) in the history of science in the 1940s and 1950s due to (...) the computer, I will emphasize the continuities with older practices and develop a two-fold argument. Firstly, one can find a diversity of practices around ENIAC which tends to be ignored if one focuses only on the ENIAC itself as the originator of Monte Carlo simulation. Following from this, I claim, secondly, that there was no simulation around ENIAC. Not only is the term ‘simulation’ not used within that context, but the analysis also shows how ‘simulation’ is an effect of three interrelated sets of different practices around the machine: (1) the mathematics which the ENIAC users employed and developed, (2) the programs, (3) the physicality of the machine. I conclude that, in the context discussed, the most important shifts in practice are about rethinking existing computational methods. This was done in view of adapting them to the high-speed and programmability of the new machine. Simulation then is but one facet of this process of adaptation, singled out by posterity to be viewed as its principal aspect. (shrink)

Embodied cognition: assumptions, theses and challenges: The paper aims at providing a concise presentation of the concept of embodied cognition that emerged in the cognitive sciences a few decades ago and has gained great popularity among empirically and philosophically informed researchers. The term “embodied cognition” is used by the author in two senses. The narrow sense implies that the body plays an important role in the process of cognition. In the broad sense “embodied cognition” is to characterize the general tendency (...) within cognitive science which finds its articulation in the 4E perspective on cognition. The working hypothesis of the 4E perspective is that cognition depends on the characteristics of the agent’s body and its interaction with the physical and social environment. It emphasizes that cognition is: embodied, embedded, enacted, extended. After reconstructing the key concepts and basic assumptions the author offers a brief appraisal of the views under discussion. He claims that the characteristics of such a trend are incomplete and not homogeneity since the perspective encompasses at least a few related and partly overlapping views on cognition. The author concludes that “embodied cognition” serves as a label for a variety of research programs within cognitive science rather than a strictly defined and well-established research tradition or a new paradigm. (shrink)

In the popular imagination, the relevance of Turing's theoretical ideas to people producing actual machines was significant and appreciated by everybody involved in computing from the moment he published his 1936 paper ‘On Computable Numbers’. Careful historians are aware that this popular conception is deeply misleading. We know from previous work by Campbell-Kelly, Aspray, Akera, Olley, Priestley, Daylight, Mounier-Kuhn, Haigh, and others that several computing pioneers, including Aiken, Eckert, Mauchly, and Zuse, did not depend on Turing's 1936 universal-machine concept. Furthermore, (...) it is not clear whether any substance in von Neumann's celebrated 1945 ‘First Draft Report on the EDVAC’ is influenced in any identifiable way by Turing's work. This raises the questions: When does Turing enter the field? Why did the Association for Computing Machinery honor Turing by associating his name to ACM's most prestigious award, the Turing Award? Previous authors have.. (shrink)

This paper examines whether a classical model could be translated into a PDP network using a standard connectionist training technique called extra output learning. In Study 1, standard machine learning techniques were used to create a decision tree that could be used to classify 8124 different mushrooms as being edible or poisonous on the basis of 21 different Features (Schlimmer, 1987). In Study 2, extra output learning was used to insert this decision tree into a PDP network being trained on (...) the identical problem. An interpretation of the trained network revealed a perfect mapping from its internal structure to the decision tree, representing a precise translation of the classical theory to the connectionist model. In Study 3, a second network was trained on the mushroom problem without using extra output learning. An interpretation of this second network revealed a different algorithm for solving the mushroom problem, demonstrating that the Study 2 network was indeed a proper theory translation. (shrink)

A key question in studying consciousness is how neural operations in the brain can identify streams of sensory input as belonging to distinct modalities, which contributes to the representation of qualitatively different experiences. The basis for identification of modalities is proposed to be constituted by self-organized comparative operations across a network of unimodal and multimodal sensory areas. However, such network interactions alone cannot answer the question how sensory feature detectors collectively account for an integrated, yet phenomenally differentiated experiential content. This (...) problem turns out to be different from, although related to, the binding problem. It is proposed that the neural correlate of an enriched, multimodal experience is constituted by the attractor state of a dynamic associative network. Within this network, unimodal and multimodal sensory maps continuously interact to influence each other’s attractor state, so that a feature change in one modality results in a fast re-coding of feature information in another modality. In this scheme, feature detection is coded by firing-rate, whereas firing phase codes relational aspects. (shrink)

A neural network is a model of the brain’s cognitive process, with a highly interconnected multiprocessor architecture. The neural network has incredible potential, in the view of these artificial neural networks inherently having good learning capabilities and the ability to learn different input features. Based on this, this paper proposes a new chaotic neuron model and a new chaotic neural network model. It includes a linear matrix, a sine function, and a chaotic neural network composed of three chaotic neurons. One (...) of the chaotic neurons is affected by the sine function. The network has rich chaotic dynamics and can produce multiscroll hidden chaotic attractors. This paper studied its dynamic behaviors, including bifurcation behavior, Lyapunov exponent, Poincaré surface of section, and basins of attraction. In the process of analyzing the bifurcation and the basins of attraction, it was found that the network demonstrated hidden bifurcation phenomena, and the relevant properties of the basins of attraction were obtained. Thereafter, a chaotic neural network was implemented by using FPGA, and the experiment proved that the theoretical analysis results and FPGA implementation were consistent with each other. Finally, an energy function was constructed to optimize the calculation based on the CNN in order to provide a new approach to solve the TSP problem. (shrink)

Long-Term Potentiation (LTP) is a kind of synaptic plasticity that many contemporary neuroscientists believe is a component in mechanisms of memory. This essay describes the discovery of LTP and the development of the LTP research program. The story begins in the 1950's with the discovery of synaptic plasticity in the hippocampus (a medial temporal lobe structure now associated with memory), and it ends in 1973 with the publication of three papers sketching the future course of the LTP research program. The (...) making of LTP was a protracted affair. Hippocampal synaptic plasticity was initially encountered as an experimental tool, then reported as a curiosity, and finally included in the ontic store of the neurosciences. Early researchers were not investigating the hippocampus in search of a memory mechanism; rather, they saw the hippocampus as a useful experimental model or as a structure implicated in the etiology of epilepsy. The link between hippocampal synaptic plasticity and learning or memory was a separate conceptual achievement. That link was formulated in at least three different ways at different times: reductively (claiming that plasticity is identical to learning), analogically (claiming that plasticity is an example or model of learning), and mechanistically (claiming that plasticity is a component in learning or memory mechanisms). The hypothesized link with learning or memory, coupled with developments in experimental techniques and preparations, shaped how researchers understood LTP itself. By 1973, the mechanistic formulation of the link between LTP and memory provided an abstract framework around which findings from multiple perspectives could be integrated into a multifield research program. (shrink)

Alan Turing anticipated many areas of current research incomputer and cognitive science. This article outlines his contributionsto Artificial Intelligence, connectionism, hypercomputation, andArtificial Life, and also describes Turing's pioneering role in thedevelopment of electronic stored-program digital computers. It locatesthe origins of Artificial Intelligence in postwar Britain. It examinesthe intellectual connections between the work of Turing and ofWittgenstein in respect of their views on cognition, on machineintelligence, and on the relation between provability and truth. Wecriticise widespread and influential misunderstandings of theChurch–Turing thesis (...) and of the halting theorem. We also explore theidea of hypercomputation, outlining a number of notional machines thatcompute the uncomputable. (shrink)

This article provides a detailed analysis of the transfer of a key cluster of ideas from mathematical logic to computing. We demonstrate the impact of certain of Turing’s logico-philosophical concepts from the mid-1930s on the emergence of the modern electronic computer—and so, in consequence, Turing’s impact on the direction of modern philosophy, via the computational turn. We explain why both Turing and von Neumann saw the problem of developing the electronic computer as a problem in logic, and we describe their (...) joint journey from logic to electronic computation. While much has been written about Turing’s and von Neumann’s individual contributions to the development of the computer, this article investigates less well-known terrain: their interactions and mutual influences. Along the way we argue against ‘logic skeptics’ and ‘Turing skeptics’, who claim that neither logic nor Turing played any significant role in the creation of the modern computer. (shrink)

It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks unorganised machines. By the application of what he described as appropriate interference, mimicking education an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of neurons is sufficient. Turing proposed simulating both the behaviour of the (...) network and the training process by means of a computer program. We outline Turing's connectionist project of 1948. (shrink)

It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks 'unorganised machines'. By the application of what he described as 'appropriate interference, mimicking education' an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of 'neurons' is sufficient. Turing proposed simulating both the behaviour of the (...) network and the training process by means of a computer program. We outline Turing's connectionist project of 1948. (shrink)

Accelerating Turing machines are Turing machines of a sort able to perform tasks that are commonly regarded as impossible for Turing machines. For example, they can determine whether or not the decimal representation of contains n consecutive 7s, for any n; solve the Turing-machine halting problem; and decide the predicate calculus. Are accelerating Turing machines, then, logically impossible devices? I argue that they are not. There are implications concerning the nature of effective procedures and the theoretical limits of computability. Contrary (...) to a recent paper by Bringsjord, Bello and Ferrucci, however, the concept of an accelerating Turing machine cannot be used to shove up Searle's Chinese room argument. (shrink)

The implications of Warren McCulloch's 1945 concept of heterarchy are analyzed in terms of human value and motivational systems. The results demonstrate the near-impossibility of predicting behavior on the basis of any hierarchical scheme, or even which among a set of hierarchical schemes will be selected as the basis of a behavioral choice. Thus, for example, people regularly say one thing and do another.

Various threshold Boolean networks, a formalism used to model different types of biological networks, can produce similar dynamics, i.e. share same behaviors. Among them, some are complex, others not. By computing both structural and behavioral complexities, we show that most TBNs are structurally complex, even those having simple behaviors. For this purpose, we developed a new method to compute the structural complexity of a TBN based on estimates of the sizes of equivalence classes of the threshold Boolean functions composing the (...) TBN. (shrink)

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...) shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter? (shrink)

This article argues that agent-based modeling is the methodological implication of Lawson’s championed ontological turn in economics. We single out three major properties of agent-based computational economics, namely, autonomous agents, social interactions, and the micro-macro links, which have been well accepted by the ACE community. We then argue that ACE does make a full commitment to the ontology of economics as proposed by Lawson, based on his prompted critical realism. Nevertheless, the article also points out the current limitations or constraints (...) of ACE. Efforts to overcome them are deemed to be crucial before ACE can make itself more promising to the current ontological turn in economics. (shrink)

The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the health status (...) of the power grid sensor network. The LPR-MLP model consists of two parts: local binary pattern, principal component analysis, and ReliefF are used to process image data and meteorological and mechanical data and the multilayer perceptron method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods. (shrink)

Philosophers of neuroscience have traditionally described interfield integration using reduction models. Such models describe formal inferential relations between theories at different levels. I argue against reduction and for a mechanistic model of interfield integration. According to the mechanistic model, different fields integrate their research by adding constraints on a multilevel description of a mechanism. Mechanistic integration may occur at a given level or in the effort to build a theory that oscillates among several levels. I develop this alternative model using (...) a putative exemplar of reduction in contemporary neuroscience: the relationship between the psychological phenomena of learning and memory and the electrophysiological phenomenon known as Long-Term Potentiation. A new look at this historical episode reveals the relative virtues of the mechanistic model over reduction as an account of interfield integration. (shrink)

The counterfactual account of physical computation is simple and, for the most part, very attractive. However, it is usually thought to trivialize the notion of physical computation insofar as it implies ‘limited pancomputationalism’, this being the doctrine that every deterministic physical system computes some function. Should we bite the bullet and accept limited pancomputationalism, or reject the counterfactual account as untenable? Jack Copeland would have us do neither of the above. He attempts to thread a path between the two horns (...) of the dilemma by buttressing the counterfactual account with extra conditions intended to block certain classes of deterministic physical systems from qualifying as physical computers. His theory is called the ‘algorithm execution account’. Here we show that the algorithm execution account entails limited pancomputationalism, despite Copeland’s argument to the contrary. We suggest, partly on this basis, that the counterfactual account should be accepted as it stands, pancomputationalist warts and all. (shrink)

Does the Wright & Liley model predict: (1) that subdural and hippocampal EEGs coherence tend to rise and fall in parallel for many frequencies, (2) that it is locally high or low within 10mm and falls steeply on average or, (3) that it is in constant flux, mostly rising and falling within 5–15 sec?

A novel conceptual framework is introduced for the Complexity Levels Theory in a Categorical Ontology of Space and Time. This conceptual and formal construction is intended for ontological studies of Emergent Biosystems, Super-complex Dynamics, Evolution and Human Consciousness. A claim is defended concerning the universal representation of an item’s essence in categorical terms. As an essential example, relational structures of living organisms are well represented by applying the important categorical concept of natural transformations to biomolecular reactions and relational structures that (...) emerge from the latter in living systems. Thus, several relational theories of living systems can be represented by natural transformations of organismic, relational structures. The ascent of man and other living organisms through adaptation, is viewed in novel categorical terms, such as variable biogroupoid representations of evolving species. Such precise but flexible evolutionary concepts will allow the further development of the unifying theme of local-to-global approaches to highly complex systems in order to represent novel patterns of relations that emerge in super- and ultra-complex systems in terms of compositions of local procedures. Solutions to such local-to-global problems in highly complex systems with ‘broken symmetry’ might be possible to be reached with the help of higher homotopy theorems in algebraic topology such as the generalized van Kampen theorems (HHvKT). Categories of many-valued, Łukasiewicz-Moisil (LM) logic algebras provide useful concepts for representing the intrinsic dynamic ‘asymmetry’ of genetic networks in organismic development and evolution, as well as to derive novel results for (non-commutative) Quantum Logics. Furthermore, as recently pointed out by Baianu and Poli (Theory and applications of ontology, vol 1. Springer, Berlin, in press), LM-logic algebras may also provide the appropriate framework for future developments of the ontological theory of levels with its complex/entangled/intertwined ramifications in psychology, sociology and ecology. As shown in the preceding two papers in this issue, a paradigm shift towards non-commutative, or non-Abelian, theories of highly complex dynamics—which is presently unfolding in physics, mathematics, life and cognitive sciences—may be implemented through realizations of higher dimensional algebras in neurosciences and psychology, as well as in human genomics, bioinformatics and interactomics. (shrink)

A novel conceptual framework is introduced for the Complexity Levels Theory in a Categorical Ontology of Space and Time. This conceptual and formal construction is intended for ontological studies of Emergent Biosystems, Super-complex Dynamics, Evolution and Human Consciousness. A claim is defended concerning the universal representation of an item’s essence in categorical terms. As an essential example, relational structures of living organisms are well represented by applying the important categorical concept of natural transformations to biomolecular reactions and relational structures that (...) emerge from the latter in living systems. Thus, several relational theories of living systems can be represented by natural transformations of organismic, relational structures. The ascent of man and other living organisms through adaptation, is viewed in novel categorical terms, such as variable biogroupoid representations of evolving species. Such precise but flexible evolutionary concepts will allow the further development of the unifying theme of local-to-global approaches to highly complex systems in order to represent novel patterns of relations that emerge in super- and ultra-complex systems in terms of compositions of local procedures. Solutions to such local-to-global problems in highly complex systems with ‘broken symmetry’ might be possible to be reached with the help of higher homotopy theorems in algebraic topology such as the generalized van Kampen theorems (HHvKT). Categories of many-valued, Łukasiewicz-Moisil (LM) logic algebras provide useful concepts for representing the intrinsic dynamic ‘asymmetry’ of genetic networks in organismic development and evolution, as well as to derive novel results for (non-commutative) Quantum Logics. Furthermore, as recently pointed out by Baianu and Poli (Theory and applications of ontology, vol 1. Springer, Berlin, in press), LM-logic algebras may also provide the appropriate framework for future developments of the ontological theory of levels with its complex/entangled/intertwined ramifications in psychology, sociology and ecology. As shown in the preceding two papers in this issue, a paradigm shift towards non-commutative, or non-Abelian, theories of highly complex dynamics—which is presently unfolding in physics, mathematics, life and cognitive sciences—may be implemented through realizations of higher dimensional algebras in neurosciences and psychology, as well as in human genomics, bioinformatics and interactomics. (shrink)

Though it''s difficult to agree on the exact date of their union, logic and artificial intelligence (AI) were married by the late 1950s, and, at least during their honeymoon, were happily united. What connubial permutation do logic and AI find themselves in now? Are they still (happily) married? Are they divorced? Or are they only separated, both still keeping alive the promise of a future in which the old magic is rekindled? This paper is an attempt to answer these questions (...) via a review of six books. Encapsulated, our answer is that (i) logic and AI, despite tabloidish reports to the contrary, still enjoy matrimonial bliss, and (ii) only their future robotic offspring (as opposed to the children of connectionist AI) will mark real progress in the attempt to understand cognition. (shrink)

Diagrams, schemata, itineraries, we are accustomed to forms of arrowheads in representation. These forms often evoke movements that writing necessarily freezes on the paper while they propose to themselves to express their dynamics. Recent progress in techniques of communication and of expression permit us at limes to surmount these difficulties. When they can spread out into space (and lime) of a propable to entrer in its turn into movement (a machine – or simply a hand), this “liberated” forms permit us (...) to remove (or at the every least to rattle) important epistemological obstacles which subsist in practice as in theory. (shrink)

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 intelligent.