Trajectories-State: A New Neural Mechanism to Interpretate Cerebral Dynamics

Abstract

With regard to neural networks, there are two different areas which have generated two lines of research. One research interest comes from the field of computer science which seeks to create and design neural networks capable of performing computational tasks. In this line of research, any neural network is relevant because the important issue is the problems which they are capable of resolving. Thus, neural networks are computational devices and computational power and the computational process which they perform are researched. The other interest of research is related to neuroscience. This focuses on both neural and brain activity. The big difference between these two lines of research can be observed from the outset. In the first, the neural network is designed and its performance on computational tasks is then researched. In the second, performance on computational tasks is known but the neural mechanism is not and neuroscience seeks to identify it. An interaction between these two lines of research is very positive because it produces synergies which generate important advances in both lines of research e.g. Hopfield’s networks. This article enunciates a neural mechanism to interpret neural dynamics based on some of the results produced by computer science. This mechanism identifies an internal or external state s with a formal language L. Independently, if the mechanism exist or not in the human brain, this mechanism can be used to design new architectures for neural networks.