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Quadratic programming of tuning curves:
a theory for tuning curve shape
Firing rate tuning curves come in many shapes and sizes, from bumpshaped to sigmoidal, from sharply peaked to broad. What are the
functional roles of these many shapes? This question has been central to
neuroscience since the first firing rate recordings of Lord Adrian in 1928.
In this project, we will turn this question on its head, and ask: how should
tuning curves be shaped, for a given function? We will assume that
function performance can be quantified with a quadratic cost function,
and we will calculate the tuning curves that minimise this cost under some
linear biophysical constraints.
This is a quadratic programming problem, a standard problem in computer
science. Merging quadratic programming with computational
neuroscience promises to lead to new insights into tuning curve function.