A Model of Neural Inspiration for Local Accumulative Computation

This paper explores the computational capacity of a novel local computational model that expands the conventional analogical and logical dynamic neural models, based on the charge and discharge of a capacity or in the use of a D flip-flop. The local memory capacity is augmented to behave as an S states automaton and some control elements are added to the memory. The analogical or digital calculus equivalent part of the balance between excitation and inhibition is also generalised to include the measure of specific spatio-temporal features over temporal expansions of the input space (dendritic field). This model is denominated as accumulative computation and is inspired in biological short-term memory mechanisms. The work describes the model’s general specifications, including its architecture, the different working modes and the learning parameters. Then, some possible software and hardware implementations (using FPGAs) are proposed, and, finally, its potential usefulness in real time motion detection tasks is illustrated.