Characterization of molecular communications among implantable biomedical neuro-inspired nanodevices
In the next future nanodevices are expected to be implanted in the human body and communicate with each other as well as with biological entities, e.g. neuronal cells, thus opening new frontiers for disease treatment, especially in neurological therapy and for drug delivery. Moreover, considering that these nanoscale devices will be small in size, will have limitations in terms of energy consumption and processing and will be injected into a biological system, they will be not able to use traditional electromagnetic or acoustic communications paradigms: rather, they will employ communication schemes similar to those used by neuronal cells and based on molecule exchange. With respect to this, a theoretical work is required to identify the information bounds for nanoscale neuronal communications. In previous papers, achievable information rates of active and passive transport in molecular communication systems have been investigated in the hypothesis of considering two nanodevices which exchange information through molecules released by a transmitter and diffused according to a Brownian motion or using molecular motors. Stochasticity in the diffusion process of these molecules causes noise in the communication among these nanodevices. In this paper we address the derivation of information bounds by introducing a realistic neuron-like communication model which takes into account interactions among nanodevices that can be implanted in the human body and, like neurons, can be simultaneously connected through thousands of synapses. In particular, an accurate characterization of the communication channel is derived and the estimation of the capacity bounds is achieved.
neuronal communications nanonetworks nanomachines neuronal noise channel capacity