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      On Reverse Engineering in the Cognitive and Brain Sciences

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          Abstract

          Various research initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms and artificial cognitive systems. This paper reviews key features of the standard method applied to complexity in the cognitive and brain sciences, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood by applying the standard method. Actually, recent findings in the experimental and theoretical fields, question central assumptions and hypotheses made for reverse engineering. Using the modeling relation as analyzed by Robert Rosen, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e. complex cognitive systems can be analyzed, understood and duplicated by reverse engineering, must be abandoned. Implications for investigations of organisms and behavior as well as for engineering artificial cognitive systems are discussed.

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          Most cited references12

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          The blue brain project.

          IBM's Blue Gene supercomputer allows a quantum leap in the level of detail at which the brain can be modelled. I argue that the time is right to begin assimilating the wealth of data that has been accumulated over the past century and start building biologically accurate models of the brain from first principles to aid our understanding of brain function and dysfunction.
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            Operational principles of neurocognitive networks.

            Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.
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              Functional ontologies for cognition: The systematic definition of structure and function.

              Cognitive scientists have traditionally specified the functional components of cognitive skills on the basis of behavioural studies of normal and neurologically impaired subjects. The results of functional imaging studies are challenging these classical models because there is a high degree of overlap among the neural systems activated by tasks that share no cognitive components. This suggests that a given neuronal structure can perform multiple functions that depend on the areas with which it interacts. However, there will be a limited range of functions that an area can perform given that its anatomical (intrinsic and extrinsic) connectivity is fixed. Assigning labels that encompass the operations that each area performs should enable a task to be re-described in terms of the functions of the areas activated. In other words, function should predict the structure and conversely structure should predict function. These systematic descriptions are referred to as ontologies. We argue that a systematic ontology for cognition would facilitate the integration of cognitive and anatomical models and organise the cognitive components of diverse tasks into a single framework. These points are illustrated with cognitive and anatomical models of reading and object recognition.
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                Author and article information

                Journal
                23 January 2012
                Article
                1201.4896
                a59cffd8-8cd3-4f30-8027-537ea2160542

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                19 pages, 5 figures
                nlin.AO q-bio.NC

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