Andrea K. Stacy, Nathan A. Schneider, Noah K. Gilman +1·Published Nov 3, 2026·10.63884/ndic.2026.6ne2oiod
Selectivity for direction of motion is a key feature of primary visual cortical neurons. Visual experience is required for direction selectivity in carnivore and primate visual cortex, but the circuit mechanisms of its formation remain incompletely understood. Here, we examined how developing lateral geniculate nucleus (LGN) neurons may contribute to cortical direction selectivity. Using in vivo electrophysiology techniques, we examined LGN receptive field properties of visually naive female ferrets before and after exposure to 6 h of motion stimuli to assess the effect of acute visual experience on LGN cell development. We found that acute experience with motion stimuli did not significantly affect the weak orientation or direction selectivity of LGN neurons. In addition, we found that neither latency nor sustainedness or transience of LGN neurons significantly changed with acute experience. These results suggest that the direction selectivity that emerges in cortex after acute experience is computed in cortex and cannot be explained by changes in LGN cells. This NDI dataset contains the recordings of the study above (Stacy, Schneider, Gilman and Van Hooser, 2023, J Neurosci 43(19):3495-3508), imported from the original laboratory files. It includes 25 of the study's 29 animals: the other 4 contributed no cells to the paper, because none of their cells passed the study's inclusion criteria, and they are not included here. The raw data are byte-for-byte the same as in the original study. The spike times are the original spike-sorted times, unchanged except for conversion to each recording's own clock; counted in each stimulus window, they give the same number of spikes on every presentation as the original analysis, and the stimulus onsets agree with the original ones to within 0.3 ms. The stimulus analysis in this dataset (stimulus decoding, stimulus responses, tuning curves and fits) has been re-run with current analysis code built on NDI. The results are very similar to the published ones but not byte-for-byte identical, because this version makes a few different choices. Where two stimuli give nearly equal responses, this version can choose a different one as the best stimulus. This version chooses the best spatial frequency before choosing between the F0 and F1 responses, where the original analysis chose between F0 and F1 first. This version subtracts a control F1 response for each stimulus, which the original analysis did not, and it measures F1 as the exact Fourier coefficient at each stimulus's temporal frequency, where the original analysis read the nearest frequency bin of a Fourier transform.
National Institutes of Health, National Eye Institute, grant EY022122
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