Publication:
Rate region analysis of multi-terminal neuronal nanoscale molecular communication channel

dc.contributor.coauthorAkan, Ozgur B.
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorKoca, Çağlar
dc.contributor.kuauthorRamezani, Hamideh
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-11-10T00:07:28Z
dc.date.issued2017
dc.description.abstractIn this paper, we investigate the communication channel capacity among hippocampal pyramidal neurons. To this aim, we study the processes included in this communication and model them with realistic communication system components based on the existing reports in the physiology literature. We consider the communication between two neurons and reveal the effects of the existence of multiple terminals between these neurons on the achievable rate per spike. To this objective, we derive the power spectral density (PSD) of the signal in the output neuron and utilize it to calculate the rate region of the channel. Moreover, we evaluate the impacts of vesicle availability on the achievable rate by deriving the expected number of available vesicles in input neuron using a realistic vesicle release model. Simulation results show that number of available vesicles for release does not affect the achievable rate of neuro-spike communication with univesicular release model. However, in neurons that multiple vesicles can release from each synaptic terminal, achievable rate is significantly affected by depletion of vesicles. Moreover, we show that increasing the number of synaptic terminals between two neurons makes the synaptic connection stronger. Hence, it is an important factor in learning and memory, which occur in the hippocampal region of the brain based on the synaptic connectivity.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipERC project MINERVA (ERC-CoG) [616922]
dc.description.sponsorshipEU project CIRCLE (EU-H2020-FET-Open) [665564]
dc.description.sponsorshipTFIBITAK graduate scholarship program [BIDEB-2215] This work was supported in part by ERC project MINERVA (ERC-2013-CoG #616922), EU project CIRCLE (EU-H2020-FET-Open #665564), and TFIBITAK graduate scholarship program (BIDEB-2215).
dc.identifier.doi10.1109/NANO.2017.8117337
dc.identifier.isbn9781-5090-3028-6
dc.identifier.scopus2-s2.0-85032193136
dc.identifier.urihttps://doi.org/10.1109/NANO.2017.8117337
dc.identifier.urihttps://hdl.handle.net/20.500.14288/16769
dc.identifier.wos434647500013
dc.keywordsCommunication channels (information theory)
dc.keywordsNanotechnology
dc.keywordsSpectral density
dc.keywordsLearning and memory
dc.keywordsMolecular communication
dc.keywordsPower spectral densities (PSD)
dc.keywordsRealistic communication
dc.keywordsSynaptic connections
dc.keywordsSynaptic connectivity
dc.keywordsSynaptic terminals
dc.keywordsSystem components
dc.keywordsNeurons
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof2017 IEEE 17th International Conference on Nanotechnology, NANO 2017
dc.subjectEngineering, electrical and electronic
dc.subjectNanoscience
dc.subjectNanotechnology
dc.titleRate region analysis of multi-terminal neuronal nanoscale molecular communication channel
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorRamezani, Hamideh
local.contributor.kuauthorKoca, Çağlar
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit2Graduate School of Sciences and Engineering
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relation.isParentOrgUnitOfPublication.latestForDiscovery434c9663-2b11-4e66-9399-c863e2ebae43

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