Event-based or neuro-inspired motor control systems communicate information through pulses, instantaneous bursts of electrical current that encode information very efficiently, as occurs in biological organisms. This work proposes a software implementation for sparse events closed-loop neuromorphic motor control theory (SECLOC) in a software tool. The control algorithm has been included in a simulator developed in Python, and publicly available, of a robotic cart controlling an inverted pendulum. The proposed implementations can be easily ported to any FPGA or microcontroller-based board and allow control of the real robotic cart. We have tested and verified the functionality using the algorithm to generate control events for proportional-integral-derivative (PID) and linear-quadratic-regulator (LQR) controllers and obtained the parameters equivalent to the proportionality, integrative, and derivative constants for these pulsing controllers. The empirical results demonstrate the validity of both the theory in simulation and its test in an actual robot in the pulsed domain. In the simulation, for angle control using a PID, with a minimum reaction time of 1 ms from the controller, the pendulum remains in equilibrium along the cart pole travel for more than 5 min, registering the 0-degree passage more than 2800 times. For the real pendulum used, the LQR controller, and the optimal control constants, with a minimum reaction time of 6 ms of the controller, the pendulum remains in equilibrium along the carriage path, as well as under slight and moderate perturbations without dropping during all experiments performed over a simulated period of almost 3.5 days.