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This paper is devoted to developing a biological-based algorithm to simulate the control of a human arm by means of a Spiking Neural Network (SNN) with a pre-set structure similar to those found in reflex arcs. The replication of the behavior of biological systems is a primary step to understand how the brain and the neural networks that comprise it work. The study of these systems began with the analysis of animals with low neural complexity as well as with small control neural networks such as those present in the reflex acts of the human body. However, the study of the brain, due to its intricate structure, still presents many unknowns and challenges to be solved. Meanwhile, one way to understand how biological systems work is to emulate their behavior through computer simulations. Artificial neural networks (ANNs) offered the opportunity to replicate neural structures to understand and reproduce their behavior and performance. Many types of ANNs are based on the use of activation functions. However, these ANNs are simplified models that do not replicate the behavior of complex biological neural systems accurately. For this reason, spike-based models have been developed to reproduce real biological systems more faithfully. This work proposes simulating the motor behavior of the central nervous system to control the position of an arm. To this end, a spiking neural network has been developed to emulate motion control using a fixed structure that reproduces reflex arcs. A channel-based synapse model and a control scheme based on the equilibrium point hypothesis have been proposed to improve biological similarity of the controller. Furthermore, the developed controller has learning capabilities thanks to the reproduction of the synapse plasticity process that takes place in real biological systems through a supervised Spike-Timing Dependent Plasticity (STDP) learning strategy. The performance of the proposed approach has been demonstrated by simulating the control of the movement of an arm using Hill's Muscle Model. Results suggest that the proposed control algorithm resembles the response of biological systems adequately in terms of speed and temporal response. [ABSTRACT FROM AUTHOR]