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A Communication Theoretic Analysis of Synaptic Channels under Axonal Noise [Article]

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dc.contributor.author Maham, Behrouz
dc.date.accessioned 2019-09-02T09:01:28Z
dc.date.available 2019-09-02T09:01:28Z
dc.date.issued 2015-09-10
dc.identifier.citation Maham, B. (2015). A Communication Theoretic Analysis of Synaptic Channels Under Axonal Noise. IEEE Communications Letters, 19(11), 1901–1904. https://doi.org/10.1109/lcomm.2015.2478006 en_US
dc.identifier.issn 1558-2558
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/4203
dc.description.abstract Molecular communication is an emerging communication technology for applications requiring nanoscale networks. Transferring vital information about external and internal conditions of the body through the nervous system is an important type of intra-body molecular nanonetworks. Thus, investigating the performance of such systems from the communication theoretic perspective gives us insight on the limitation of neuro-spike communication and ways to design artificial neural systems. In this letter, we study the performance of the neuro-spike communication under different stochastic impairments such as axonal shot noise, synaptic noise, and random vesicle release. The objective is to optimally detect the spikes at the receiving neuron. Since several uncertainties occur under each hypothesis, composite hypothesis is employed to find the optimum detection policy. Furthermore, we obtain closed-form solutions for the optimal detector and derive the binary decision error at the postsynaptic terminal. en_US
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Molecular communication en_US
dc.subject nervous system en_US
dc.subject intra-body molecular nanonetworks en_US
dc.subject axonal shot noise en_US
dc.title A Communication Theoretic Analysis of Synaptic Channels under Axonal Noise [Article] en_US
dc.type Article en_US
workflow.import.source science


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