Are There Universal Principles of Brain Computation?

Abstract

Are there universal computational principles that the brain uses to self-organize its intelligent properties? This lecture suggests that common principles are used in brain systems for early vision, visual object recognition, auditory source identification, variable-rate speech perception, and adaptive sensory-motor control, among others. These are principles of matching and resonance that form part of Adaptive Resonance Theory, or ART. In particular, bottom-up signals in an ART system can automatically activate target cells to levels capable of generating suprathresh-old output signals. Top-down expectation signals can only excite, or prime, target cells to subthreshold levels. When both bottom-up and top-down signals are simultaneously active, only the bottom-up signals that receive top-down support can remain active. All other cells, even those receiving large bottom-up inputs, are inhibited. Top-down matching hereby generates a focus of attention that can resonate across processing levels, including those that generate the top-down signals. Such a resonance acts as a trigger that activates learning processes within the system. In the examples described herein, these effects are due to a top-down nonspecific inhibitory gain control signal that is released in parallel with specific excitatory signals.