Communication theoretical modeling and analysis of neuronal communication with synaptic plasticity

Emergence of nanoscale devices and their applications mandates the facilitation of communication between these devices, and hence, the development of nanonetworks, to overcome their computational and power limitations owing to their size. The nanonetworks need novel communication paradigms since they are fundamentally different from the traditional communication networks. The human body is a large-scale network of molecular nanonetworks composed of billions of nanomachines, i.e., cells, where molecules are used to encode, transmit and receive information. The largest and the most vital intra-body nanonetwork is the nervous nanonetwork. Thus, the aim of this thesis is to investigate the nervous nanonetwork from information and communication theoretical perspective, to lay down the foundations of a novel bio-inspired communication paradigm for nanonetworks. We focus on the communication theoretical analysis of single-input-single-output (SISO) as well as multiple-input-single-output (MISO) neuro-spike communication channels, considering memory and metabolic energy constraints. Synaptic plasticity is a ubiquitous phenomenon in central as well as peripheral nervous systems and is associated with memory and learning new behaviors and skills. Thus, we further aim to analyze the neuro-spike communication incorporating the plasticity. The results obtained from this study would allow us to select optimal parameters to realize effıcient bio-inspired nanonetworks. Moreover, since neurodegenerative diseases such as Alzheimer's Disease, Parkinson's Disease and Multiple Sclerosis are caused by the malfunction of the sub-processes of the neuro-spike communication, these results can be compared with the results obtained from diseased synapses to develop the future ICT-inspired diagnostic and treatment techniques for neural disorders.