Publication:
Synaptic interference channel

dc.contributor.coauthorN/A
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorAkan, Özgür Barış
dc.contributor.kuauthorMalak, Derya
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-09T23:49:15Z
dc.date.issued2013
dc.description.abstractSynaptic channels automatically adapt their weights to compensate for the variations resulted from the input and output characteristics, i.e., spike frequency, time correlation among inputs, time difference between presynaptic and postsynaptic action potentials. Modification of the synaptic conductances, i.e., channel weights, is the main mechanism that enables learning in neurons. In this paper, we approach this learning mechanism from a different perspective. First, we analyze the single-input single-output (SISO) and multi-input single-output (MISO) synaptic interference channels, and achievable communication rates. Furthermore, we provide the natural adaptive weight update algorithm for neurons based on experimental findings. Our results demonstrate that neurons are capable of mitigating the interference, and achieve rates close to the capacity.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipTurkish Scientific and Technical Research Council [109E257]
dc.description.sponsorshipTurkish National academy of Sciences Distinguished Young Scientist award Program (TUBa-GEBIP)
dc.description.sponsorshipIBM through IBM Faculty award This work was supported in part by the Turkish Scientific and Technical Research Council Career award under grant #109E257 and by the Turkish National academy of Sciences Distinguished Young Scientist award Program (TUBa-GEBIP) and by IBM through IBM Faculty award.
dc.identifier.doi10.1109/ICCW.2013.6649337
dc.identifier.isbn9781-4673-5753-1
dc.identifier.scopus2-s2.0-84890868435
dc.identifier.urihttps://doi.org/10.1109/ICCW.2013.6649337
dc.identifier.urihttps://hdl.handle.net/20.500.14288/14343
dc.identifier.wos572825900148
dc.keywordsAction potentials
dc.keywordsCommunication rate
dc.keywordsInput and output characteristics
dc.keywordsInterference channels
dc.keywordsLearning mechanism
dc.keywordsSingle-input
dc.keywordsSingle-output
dc.keywordsSynaptic conductance
dc.keywordsTime correlations
dc.keywordsElectrophysiology
dc.keywordsNeurons
dc.keywordsSignal interference
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof2013 IEEE International Conference on Communications Workshops, ICC 2013
dc.subjectEngineering
dc.subjectElectrical and electronic engineering
dc.subjectTelecommunications
dc.titleSynaptic interference channel
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorMalak, Derya
local.contributor.kuauthorAkan, Özgür Barış
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Electrical and Electronics Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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