Neuronal spikes are referred to as the electric activity of neurons (in terms of voltage) in response to various biological events such as the sodium and calcium ionic current channels in the brain. Currently, both biological models as well as mathematical models of neuronal spiking patterns have been introduced in the literature. However, very little attempt has been made to run these models in real-time. With applications ranging from hardware neuromorphic circuit designs, artificial intelligence (AI) architectures, to deep brain stimulation, real-time generation of these models is of particular interest in the brain-inspired computing/architecture and neuro-modulation/stimulation research communities. This paper proposes the development of a framework for generating the hyperbolic based single neuronal spiking patterns in real-time. Simulation results confirm that the generated spikes resemble the existing models of neuronal spiking patterns, with additional real-time run capability as well as the ability to change the parameters on the fly. Clinical relevance-Real-time models of neuronal spiking patterns have significant clinical relevance with respect to applications of neuromorphic/AI chips for medical image processing/computer vision, as well as clinical neuroscience, neuromodulation and neurostimulation such as deep brain stimulation for modulating the abnormal effects of neurological diseases.