Adaptation optimizes sensory encoding for future stimuli.
Sensory neurons continually adapt their response characteristics according to recent stimulus history. However, it is unclear how such a reactive process can benefit the organism. Here, we test the hypothesis that adaptation actually acts proactively in the sense that it optimally adjusts sensory en...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012746 |
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| author | Jiang Mao Constantin A Rothkopf Alan A Stocker |
| author_facet | Jiang Mao Constantin A Rothkopf Alan A Stocker |
| author_sort | Jiang Mao |
| collection | DOAJ |
| description | Sensory neurons continually adapt their response characteristics according to recent stimulus history. However, it is unclear how such a reactive process can benefit the organism. Here, we test the hypothesis that adaptation actually acts proactively in the sense that it optimally adjusts sensory encoding for future stimuli. We first quantified human subjects' ability to discriminate visual orientation under different adaptation conditions. Using an information theoretic analysis, we found that adaptation leads to a reallocation of coding resources such that encoding accuracy peaks at the mean orientation of the adaptor while total coding capacity remains constant. We then asked whether this characteristic change in encoding accuracy is predicted by the temporal statistics of natural visual input. Analyzing the retinal input of freely behaving human subjects showed that the distribution of local visual orientations in the retinal input stream indeed peaks at the mean orientation of the preceding input history (i.e., the adaptor). We further tested our hypothesis by analyzing the internal sensory representations of a recurrent neural network trained to predict the next frame of natural scene videos (PredNet). Simulating our human adaptation experiment with PredNet, we found that the network exhibited the same change in encoding accuracy as observed in human subjects. Taken together, our results suggest that adaptation-induced changes in encoding accuracy prepare the visual system for future stimuli. |
| format | Article |
| id | doaj-art-90316d56d8324fa7beb7203a465528e3 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-90316d56d8324fa7beb7203a465528e32025-02-05T05:30:41ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101274610.1371/journal.pcbi.1012746Adaptation optimizes sensory encoding for future stimuli.Jiang MaoConstantin A RothkopfAlan A StockerSensory neurons continually adapt their response characteristics according to recent stimulus history. However, it is unclear how such a reactive process can benefit the organism. Here, we test the hypothesis that adaptation actually acts proactively in the sense that it optimally adjusts sensory encoding for future stimuli. We first quantified human subjects' ability to discriminate visual orientation under different adaptation conditions. Using an information theoretic analysis, we found that adaptation leads to a reallocation of coding resources such that encoding accuracy peaks at the mean orientation of the adaptor while total coding capacity remains constant. We then asked whether this characteristic change in encoding accuracy is predicted by the temporal statistics of natural visual input. Analyzing the retinal input of freely behaving human subjects showed that the distribution of local visual orientations in the retinal input stream indeed peaks at the mean orientation of the preceding input history (i.e., the adaptor). We further tested our hypothesis by analyzing the internal sensory representations of a recurrent neural network trained to predict the next frame of natural scene videos (PredNet). Simulating our human adaptation experiment with PredNet, we found that the network exhibited the same change in encoding accuracy as observed in human subjects. Taken together, our results suggest that adaptation-induced changes in encoding accuracy prepare the visual system for future stimuli.https://doi.org/10.1371/journal.pcbi.1012746 |
| spellingShingle | Jiang Mao Constantin A Rothkopf Alan A Stocker Adaptation optimizes sensory encoding for future stimuli. PLoS Computational Biology |
| title | Adaptation optimizes sensory encoding for future stimuli. |
| title_full | Adaptation optimizes sensory encoding for future stimuli. |
| title_fullStr | Adaptation optimizes sensory encoding for future stimuli. |
| title_full_unstemmed | Adaptation optimizes sensory encoding for future stimuli. |
| title_short | Adaptation optimizes sensory encoding for future stimuli. |
| title_sort | adaptation optimizes sensory encoding for future stimuli |
| url | https://doi.org/10.1371/journal.pcbi.1012746 |
| work_keys_str_mv | AT jiangmao adaptationoptimizessensoryencodingforfuturestimuli AT constantinarothkopf adaptationoptimizessensoryencodingforfuturestimuli AT alanastocker adaptationoptimizessensoryencodingforfuturestimuli |