Spike-based neural networks have been shown to hold more computational power than other architectures and their integration into mainstream computing is projected to herald a new age for information technology. Implementation using a flexible Thin Organic Large Area Electronics TOLAE process is a promising low cost way to integrate neuromorphic circuits into sensors, however its feasibility has never been demonstrated. Here we design and simulate a spiking neuron circuit that can be implemented using pOTFT TOLAE processes. Our spiking neuron circuit is inspired by the Morris-Lecar model. Two transistors model the flow in or out of ions onto a simulated membrane potential, two capacitors provide distinct time constants for the two competing processes and support circuits direct an excitation current towards the control transistors. The transistors are operated in the deep subthreshold regime at V < |3.5V| and currents ranging from 10 pA and 5 nA. To determine the appropriate dimensions and model parameters for the transistors in the circuit, we performed a systematic fitting of 210 devices fabricated at the CEA. We thus are able to demonstrate numerically a spiking neuron circuit in an existing organic technology. We also explore how variability in transistor size, the subthreshold swing and the turn on voltage effects the circuits. We consider both whether the transistors exhibits spiking behaviour and the range of this behaviour. Our results demonstrate the feasibility of OTFT technologies for spiking neuromorphic hardware.