Why neural networks don't yet fly: inquiry into the neurodynamics of biological intelligence

Abstract

There are two documented hierarchical modes in which wetware brains sustain information for storage, transformation, and other operations. These modes are in some senses diametrically opposed but in other senses are strongly complementary. One is typified by the action potential and the point process; the other by the synaptic potential and the local mean field. In sensory systems the one is the basis for feature extraction, preprocessing, and analysis. The other is the basis for experiential integration, classification, and synthesis. Neither can supplant or function without the other. They coexist in the same layers of neurons, and whether an experimenter observes one or the other depends on the method used to acquire, process, and measure biological data. The author's aim is to exemplify these two modes of information, describe how they are derived from brains and how they are converted each to the other, and explain their significance for the design of new and more successful neural networks.