Neuroscience research has produced many theories and computational neural models of sensory nervous systems. Notwithstanding many different perspectives towards developing intelligent machines, artificial intelligence has ultimately been influenced by neuroscience. Therefore, this paper provides an introduction to biologically inspired machine intelligence by exploring the basic principles of sensation and perception as well as the structure and behavior of biological sensory nervous systems like the neocortex. Concepts like spike timing, synaptic plasticity, inhibition, neural structure, and neural behavior are applied to a new model, Simple Cortex (SC). A software implementation of SC has been built and demonstrates fast observation, learning, and prediction of spatio-temporal sensory-motor patterns and sequences. Finally, this paper suggests future areas of improvement and growth for Simple Cortex and other related machine intelligence models.