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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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      Most cited references 15

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      Homeostatic plasticity in the developing nervous system.

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        Plasticity in the intrinsic excitability of cortical pyramidal neurons.

        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

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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
            SOR-COMPSCI
            ScienceOpen Research
            ScienceOpen
            2199-1006
            08 December 2014
            : 0 (ID: b0523e94-8eec-4a4a-a9a1-e92625c17f94 )
            : 0
            : 1-8
            2177:XE
            10.14293/S2199-1006.1.SOR-COMPSCI.AP5TO7.v1
            © 2014 Y. Parzhin.

            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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