Novel nano-scale communications techniques are inspired by some naturally existing phenomena such as the molecular communication, neuro spike communication and controlling cellular signaling mechanisms. Among these, neuro-spike communication, which governs the communications between neurons, is a vastly unexplored area. In this paper, we assume a point-to-point communication model for neuro-spike communication and consider several sources of randomness to achieve a realistic model. These random factors consist of axonal noise, random vesicle release, random amplitude and synaptic noise. We model the axonal transmission part as a binary channel through defining the axonal shot noise probability. Next, we investigate the second part of neuro-spike communication channel, i.e., synaptic transmission, to derive error probability of this part. Moreover, we derive a closed form description for the decision threshold to design an optimum spike detection receiver. Then, we assume the neuro-spike communication channel as two cascaded binary channels and derive its error probability. We investigate the impact of axonal noise, synaptic noise and the probability of vesicle release on the error probability of the neuro-spike communication channel.