Modeling the dynamic processing of the presynaptic terminals for intrabody nanonetworks

Experimental evidences show that: 1) the release sites from a single axon have variable release probabilities, even when the axon contacts the same postsynaptic neuron; 2) this variability in the release probability implies a compartmentalization at the level of the presynaptic terminals of the neuronal processing; 3) the specificity of the presynaptic terminal processing is driven by and reflects the complex biophysical mechanisms activated at the axon terminals when a spike is fired in response to a stimulus. Stemming from these experimental evidences, we propose a communication engineering model for capturing the behavior of biological neurons. Specifically, by adopting a stochastic approach, the presynaptic terminals are modeled as a dynamic array of transmitters, where each transmitter models the processing specificity of a presynaptic terminal. In particular, we first show that the unique and specific processing of a presynaptic terminal can be reconducted to the cascade of a frequency selector and an amplitude modulator. Then, we characterize the propagation of the presynaptic-filtered signal through the synaptic cleft, and we derive the delay along with the channel attenuation as a function of the distance between the communicating neurons. Finally, the theoretical analysis is validated through numerical simulation