Outline (click for pdf file)

Conventional digital computers store information encoded in strings of binary digits. We propose an alternative approach of pattern-based computing, in which information is stored in the form of spatial patterns of modified connections in very large-scale networks. Memories are retrieved by phase transitions, which enable cerebral cortices to construct spatial patterns of amplitude modulation of a narrow band oscillatory carrier wave. More precisely, information is encoded by spatial patterns of 'synaptic' weights of connections that couple nonlinear processing elements. Each category of sensory input that as subject can remember has a Hebbian nerve cell assembly. When accessed by a stimulus, the assembly guides cortex into a basin in a landscape of attractors, one for each category.

Our approach to oscillating memory devices is strongly biologically motivated. It is based on observations that sensory information processing in the central nervous system is realized via collective oscillations of globally interacting neuronal populations. By using these robust biological mechanisms animals have capacities for perceiving and recognizing sensory signals that far surpass any existing man-made devices. This approach provides a novel view on neural networks. It includes as special cases other models, including deterministic cellular automata, such as Conway's Game of Life, Chua's cellular neural networks, mean field models like the Ising model, Amari's and Hopfield's neural network arrays, and Baars' global workspace model. In our tutorial we describe the foundational neurophysiological and computational issues that must be addressed in order to undertake bio-inspired modeling of brain dynamics and brain-machine interfacing.