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

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Akan, OB 

© 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.

46 Information and Computing Sciences, 32 Biomedical and Clinical Sciences, 3209 Neurosciences, 4611 Machine Learning, Neurosciences, Mental Health, 1 Underpinning research, 1.1 Normal biological development and functioning, Neurological
Journal Title
2017 IEEE 17th International Conference on Nanotechnology, NANO 2017
Conference Name
2017 IEEE 17th International Conference on Nanotechnology (IEEE-NANO)
Journal ISSN
Volume Title
European Research Council (616922)
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (665564)