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. 2013 Nov 12;11(11):e1001710. doi: 10.1371/journal.pbio.1001710

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© 2013 Rabinowitz et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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(A) Schematic of adaptive-LN model. Top/bottom, DRC stimuli. DRCs are filtered through a STRF, then passed through an output nonlinearity, yielding the firing rate (). Output nonlinearities change with stimulus contrast. Insets, example time series. (B) Example units, nonlinearities during low (blue) and high (red) contrast DRCs. Insets, STRFs. Bottom, distributions of STRF-filtered DRCs under low/high contrast. (C) Nonlinearities in (B), replotted in normalized coordinates. (D) Contrast-dependent changes to the slope of units' nonlinearities. (E) Percentage of residual signal power explained by gain kernel model above an LN model [12]. (F) Log increase in Fisher information in units' encoding of low contrast stimuli, resulting from adaptation to this distribution. Zero, no adaptation. Larger positive values, greater adaptation.

Inline graphic — (A) Schematic of adaptive-LN model. Top/bottom, DRC stimuli. DRCs are filtered through a STRF, then passed through an output nonlinearity, yielding the firing rate (). Output nonlinearities change with stimulus contrast. Insets, example time series. (B) Example units, nonlinearities during low (blue) and high (red) contrast DRCs. Insets, STRFs. Bottom, distributions of STRF-filtered DRCs under low/high contrast. (C) Nonlinearities in (B), replotted in normalized coordinates. (D) Contrast-dependent changes to the slope of units' nonlinearities. (E) Percentage of residual signal power explained by gain kernel model above an LN model [12]. (F) Log increase in Fisher information in units' encoding of low contrast stimuli, resulting from adaptation to this distribution. Zero, no adaptation. Larger positive values, greater adaptation.