• DocumentCode
    3605608
  • Title

    A Communication Theoretic Analysis of Synaptic Channels Under Axonal Noise

  • Author

    Maham, Behrouz

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Nazarbayev Univ., Astana, Kazakhstan
  • Volume
    19
  • Issue
    11
  • fYear
    2015
  • Firstpage
    1901
  • Lastpage
    1904
  • 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.
  • Keywords
    binary decision diagrams; molecular communication (telecommunication); axonal noise; binary decision error; closed-form solutions; communication theoretic analysis; composite hypothesis; intra-body molecular nanonetworks; molecular communication; neuro-spike communication; synaptic channels; Detectors; Mathematical model; Molecular communication; Neurons; Noise; Random variables; Molecular communications; nanonetworks; neuro-spike communication channel; optimal binary detection;
  • fLanguage
    English
  • Journal_Title
    Communications Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1089-7798
  • Type

    jour

  • DOI
    10.1109/LCOMM.2015.2478006
  • Filename
    7254130