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The ability of the organism to distinguish between various stimuli is limitedby the structure and noise in the population code of its sensory neurons. Herewe infer a distance measure on the stimulus space directly from the recordedactivity of 100 neurons in the salamander retina. In contrast to previouslyused measures of stimulus similarity, this "neural metric" tells us howdistinguishable a pair of stimulus clips is to the retina, given the noise inthe neural population response. We show that the retinal distance stronglydeviates from Euclidean, or any static metric, yet has a simple structure: weidentify the stimulus features that the neural population is jointly sensitiveto, and show the SVM-like kernel function relating the stimulus and neuralresponse spaces. We show that the non-Euclidean nature of the retinal distancehas important consequences for neural decoding.

Retinal metric: a stimulus distance measure derived from population neural responses