Rate region analysis of multi-terminal neuronal nanoscale molecular communication channel
View / Open Files
Publication Date
2017-11-21Journal Title
2017 IEEE 17th International Conference on Nanotechnology, NANO 2017
ISSN
1944-9399
ISBN
9781509030286
Pages
59-64
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Ramezani, H., Koca, C., & Akan, O. (2017). Rate region analysis of multi-terminal neuronal nanoscale molecular communication channel. 2017 IEEE 17th International Conference on Nanotechnology, NANO 2017, 59-64. https://doi.org/10.1109/NANO.2017.8117337
Abstract
© 2017 IEEE. In 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.
Sponsorship
European Commission FP7 ERC Consolidator Grant (616922)
Identifiers
External DOI: https://doi.org/10.1109/NANO.2017.8117337
This record's URL: https://www.repository.cam.ac.uk/handle/1810/287030
Rights
Licence:
http://www.rioxx.net/licenses/all-rights-reserved