We propose a novel Physical Unclonable Function (PUF) derived from the neuron used in the octopus retina. The signature obtained from a single neuron has been utilized to extract digital fingerprints uniquely for every single neuron. The proposed circuit topology has been investigated from a theoretical standpoint targeting new hardware-based security mechanisms obtained from intrinsic variations in the fabrication process of Spiking Neural Networks (SNNs). The uniformity, robustness and reliability of the proposed PUF have been verified by mean of the proposed analytical models aiming at developing new methodological approaches for the design of secure PUFs resilient to the adversarial Machine Learning attacks.