The ability of the organism to distinguish between various stimuli is limited
by the structure and noise in the population code of its sensory neurons. Here
we infer a distance measure on the stimulus space directly from the recorded
activity of 100 neurons in the salamander retina. In contrast to previously
used measures of stimulus similarity, this "neural metric" tells us how
distinguishable a pair of stimulus clips is to the retina, given the noise in
the neural population response. We show that the retinal distance strongly
deviates from Euclidean, or any static metric, yet has a simple structure: we
identify the stimulus features that the neural population is jointly sensitive
to, and show the SVM-like kernel function relating the stimulus and neural
response spaces. We show that the non-Euclidean nature of the retinal distance
has important consequences for neural decoding.Comment: 5 pages, 4 figures, to appear in Phys Rev Let
