Statistical multiplexing for neural nanonetworks in case of neuron specific faults

Although nanonetworking is in its infancy, a wide range of appealing application areas especially in human healthcare field draws the attention of both the medical and scientific communities. Many neurological diseases like paralysis are caused by the interruption of spike propagation due to the malfunctioning neurons in the signaling pathway where the spikes are carried. In this paper, we propose a neuron specific statistical multiplexing scheme to substitute a faulty sensory neural pathway with a neighboring functional one. Since the spikes are stereotyped events and they have no addressing information, we developed an addressing scheme utilizing the spikes themselves. The performance achieved by the proposed technique is analyzed in terms of the percentage of the spikes transmitted under various scenarios. We also compared the results obtained with the previously proposed TDMA based multiplexing schemes. The proposed statistical multiplexing based technique has lower implementation complexity than the previously introduced TDMA based techniques. Additionally, we evaluated the performance of the proposed technique when a priority mechanism is employed. The concept of multiplexing spikes to substitute a faulty neural pathway with a functional pathway reveals new opportunities in neuronal communication and may pave the way to the real healthcare applications of nanonetworking in the near future.