Neuronal tuning: To sharpen or broaden?

Sensory and motor variables are typically represented by a population of br oadly tuned neurons. A coarser representation with broader tuning can often improve coding accuracy, but sometimes the accuracy may also improve with sharper tuning. The theoretical analysis here shows that the relationship b etween tuning width and accuracy depends crucially on the dimension of the encoded variable. A general rule is derived for how the Fisher information scales with the tuning width, regardless of the exact shape of the tuning f unction, the probability distribution of spikes, and allowing some correlat ed noise between neurons. These results demonstrate a universal dimensional ity effect in neural population coding.