On the modeling of a nano communication network using spiking neural architecture

The human neural system is comprised of millions of neural cells which are fully networked through synaptic connections. Each neuron receives the input through dendrites from neighboring nodes, in order to fire an action potential through its axon into the next neurons. In this paper, a biological communication network is modeled and a scenario is implemented to detect the damaged neural cells. For this purpose, the membrane potential of the biological network nodes is monitored and evaluated using spiking neural network algorithm. A spiking Izhikevich neural modeling technique is implemented at nanoscale to model the biological network. A swarm of nanobots is taken into consideration to diagnose the malfunction of the biological communication network by measuring the membrane potential of each firing neuron and the neighboring nodes. After fault occurrence, some nodes will no longer be available to process and communicate in the biological communication network. Therefore, the fully-connected biological network as a small-world network would be a randomly-connected communication network. The idea of this research is to diagnose the defected nano-cells and autonomously cluster to regenerate a small-world network using the available neighboring neural cells. To reestablish the small-world biological communication network, a graph theory scheme is applied considering the membrane potentials and coordination of the neural cells in a two dimensional biological network. The depth-first search algorithm is implemented for clustering and the simulation results are illustrated and discussed.