Information Theoretical Analysis of Synaptic Communication for Nanonetworks

Conference Object
Change log
Khan, T 
Akan, OB 

© 2018 IEEE. Communication among neurons is the highly evolved and efficient nanoscale communication paradigm, hence the most promising technique for biocompatible nanonetworks. This necessitates the understanding of neuro-spike communication from information theoretical perspective to reach a reference model for nanonetworks. This would also contribute towards developing ICT-based diagnostics techniques for neuro-degenerative diseases. Thus, in this paper, we focus on the fundamental building block of neuro-spike communication, i.e., signal transmission over a synapse, to evaluate its information transfer rate. We aim to analyze a realistic synaptic communication model, which for the first time, encompasses the variation in vesicle release probability with time, synaptic geometry and the re-uptake of neurotransmitters by pre-synaptic terminal. To achieve this objective, we formulate the mutual information between input and output of the synapse. Then, since this communication paradigm has memory, we evaluate the average mutual information over multiple transmissions to find its overall capacity. We derive a closed-form expression for the capacity of the synaptic communication as well as calculate the capacity-achieving input probability distribution. Finally, we find the effects of variation in different synaptic parameters on the information capacity and prove that the diffusion process does not decrease the information a neural response carries about the stimulus in real scenario.

Nanonetworks, molecular communication, neuro-spike communication, information capacity, synaptic transmission
Journal Title
Proceedings - IEEE INFOCOM
Conference Name
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications
Journal ISSN
Volume Title
European Research Council (616922)
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (665564)