Neural crystal a dynamic recurrent network

Unlike feedforward MLP, recurrent networks have ability to work with temporal inputs and outputs. So the signal can be input directly to the network without preprocessing like time-windowing and without having direct dependency between signal duration and number of neurons. Crystal organisation of neurons has already been proposed by a number of neurobiologists but less work has been done to study properties of such ANNs. An important difference with recurrent multilayered networks lies in the 8 isotropic directions of the structure: it has the same topological organisation along 8 directions. The motivation for the architecture described in this paper is then to propose a model which presents the following properties: Hardware feasibility, compact matricial formalism and temporal dynamic. So we present a mathematical formalism and four examples of application in robotic and signal recognition