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

dc.contributor.authorMaham, Behrouz
dc.date.accessioned2019-09-02T09:01:28Z
dc.date.available2019-09-02T09:01:28Z
dc.date.issued2015-09-10
dc.description.abstractMolecular 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.identifier.citationMaham, 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.2015.2478006en_US
dc.identifier.issn1558-2558
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/4203
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectMolecular communicationen_US
dc.subjectnervous systemen_US
dc.subjectintra-body molecular nanonetworksen_US
dc.subjectaxonal shot noiseen_US
dc.titleA Communication Theoretic Analysis of Synaptic Channels under Axonal Noise [Article]en_US
dc.typeArticleen_US
workflow.import.sourcescience

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