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    Hypotheses of neural code and the information model of the neuron detector

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        Abstract

        This paper deals with the problem of neural code solving. On the basis of the formulated hypotheses, the information model of a neuron detector is suggested, the detector being one of the basic elements of an artificial neural network (ANN). The paper subjects the connectionist paradigm of ANN building to criticism and suggests a new presentation paradigm for ANN building and neuro-elements (NE) learning. The adequacy of the suggested model is proved by the fact that it does not contradict the modern propositions of neuropsychology and neurophysiology.

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        Homeostatic plasticity in the developing nervous system.

         Deborah Nelson,  G G Turrigiano (2004)
        Article 0 comments Cited 285 times – based on 0 reviews     Review now
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          Plasticity in the intrinsic excitability of cortical pyramidal neurons.

           G G Turrigiano,  Sachin N. Desai,  Ronny Rutherford (1999)
          During learning and development, the level of synaptic input received by cortical neurons may change dramatically. Given a limited range of possible firing rates, how do neurons maintain responsiveness to both small and large synaptic inputs? We demonstrate that in response to changes in activity, cultured cortical pyramidal neurons regulate intrinsic excitability to promote stability in firing. Depriving pyramidal neurons of activity for two days increased sensitivity to current injection by selectively regulating voltage-dependent conductances. This suggests that one mechanism by which neurons maintain sensitivity to different levels of synaptic input is by altering the function relating current to firing rate.
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            Neural Networks and Learning Machines

             SS Haykin,  S Haykin,  S.S. Haykin … (2009)
            Article 0 comments Cited 56 times – based on 0 reviews     Review now
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              Author and article information

              Affiliations
              [1 ]Department of Computer Science and Intellectual Property, Faculty of Computer and Information Technology, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv, Ukraine
              Author notes
              [* ]Corresponding author's e-mail address: pargin@ 123456mipk.kharkiv.edu
              Contributors
              Journal
              Journal ID (publisher-id): SOR-COMPSCI
              Title: ScienceOpen Research
              Publisher: ScienceOpen
              ISSN (Electronic): 2199-1006
              Publication date 08 December 2014
              Volume: 0 (ID: b0523e94-8eec-4a4a-a9a1-e92625c17f94 )
              Issue: 0
              Pages: 1-8
              Publisher ID: 2177:XE
              DOI: 10.14293/S2199-1006.1.SOR-COMPSCI.AP5TO7.v1
              Copyright statement: © 2014 Y. Parzhin.

              License:

              This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

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              Figures: 5, Tables: 0, References: 16, Pages: 8
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              Categories
              Subject: Original article

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              ScienceOpen disciplines: Computer science

              Keywords: neuron-detector, artificial neural network, neural code, artificial intelligence

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