This is a graduate course that examines neural processing with a focus on distributed mechanisms. A distributed mechanism is some computational property that only makes sense when you consider a population of units (neurons in our case). For example, neural firing rates sometimes fluctuate up and down several times a second, and this fluctuation is often synchronized between different cells. Whatever this synchrony means (no one knows), it is a distinctly multi-neuron property. A single neuron cannot be synchronized with itself.

In this course we cover various examples of distributed processing and study the mathematics of each. By "study" I mean I teach the math, emphasizing concepts rather than details so that abstruse distributed mechanisms become understandable in the light of mathematical principles. Some students realize for the first time in this class that mathematics is not about methods for calculating things so much as it is about illuminating things our intuition can't handle.