Unsupervised learning of temporal regularities in visual cortical populations
Abstract The brain’s ability to extract temporal information from dynamic stimuli in the environment is essential for everyday behavior. To extract temporal statistical regularities, neural circuits must possess the ability to measure, produce, and anticipate sensory events. Here we report that when...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60731-3 |
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| author | Sorin Pojoga Ariana Andrei Valentin Dragoi |
| author_facet | Sorin Pojoga Ariana Andrei Valentin Dragoi |
| author_sort | Sorin Pojoga |
| collection | DOAJ |
| description | Abstract The brain’s ability to extract temporal information from dynamic stimuli in the environment is essential for everyday behavior. To extract temporal statistical regularities, neural circuits must possess the ability to measure, produce, and anticipate sensory events. Here we report that when neural populations in macaque primary visual cortex are triggered to exhibit a periodic response to a repetitive sequence of optogenetic laser flashes, they learn to accurately reproduce the temporal sequence even when light stimulation is turned off. Despite the fact that individual cells had a poor capacity to extract temporal information, the population of neurons reproduced the periodic sequence in a temporally precise manner. The same neural population could learn different frequencies of external stimulation, and the ability to extract temporal information was found in all cortical layers. These results demonstrate a remarkable ability of sensory cortical populations to extract and reproduce complex temporal structure from unsupervised external stimulation even when stimuli are perceptually irrelevant. |
| format | Article |
| id | doaj-art-12f3f99410cb497c867c387f020e4e3c |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-12f3f99410cb497c867c387f020e4e3c2025-08-20T03:45:34ZengNature PortfolioNature Communications2041-17232025-07-0116111210.1038/s41467-025-60731-3Unsupervised learning of temporal regularities in visual cortical populationsSorin Pojoga0Ariana Andrei1Valentin Dragoi2Department of Neurobiology and Anatomy, McGovern Medical School, University of TexasDepartment of Neurobiology and Anatomy, McGovern Medical School, University of TexasCenter for Neural Systems Restoration, Houston Methodist Research Institute, Dept. of NeurosurgeryAbstract The brain’s ability to extract temporal information from dynamic stimuli in the environment is essential for everyday behavior. To extract temporal statistical regularities, neural circuits must possess the ability to measure, produce, and anticipate sensory events. Here we report that when neural populations in macaque primary visual cortex are triggered to exhibit a periodic response to a repetitive sequence of optogenetic laser flashes, they learn to accurately reproduce the temporal sequence even when light stimulation is turned off. Despite the fact that individual cells had a poor capacity to extract temporal information, the population of neurons reproduced the periodic sequence in a temporally precise manner. The same neural population could learn different frequencies of external stimulation, and the ability to extract temporal information was found in all cortical layers. These results demonstrate a remarkable ability of sensory cortical populations to extract and reproduce complex temporal structure from unsupervised external stimulation even when stimuli are perceptually irrelevant.https://doi.org/10.1038/s41467-025-60731-3 |
| spellingShingle | Sorin Pojoga Ariana Andrei Valentin Dragoi Unsupervised learning of temporal regularities in visual cortical populations Nature Communications |
| title | Unsupervised learning of temporal regularities in visual cortical populations |
| title_full | Unsupervised learning of temporal regularities in visual cortical populations |
| title_fullStr | Unsupervised learning of temporal regularities in visual cortical populations |
| title_full_unstemmed | Unsupervised learning of temporal regularities in visual cortical populations |
| title_short | Unsupervised learning of temporal regularities in visual cortical populations |
| title_sort | unsupervised learning of temporal regularities in visual cortical populations |
| url | https://doi.org/10.1038/s41467-025-60731-3 |
| work_keys_str_mv | AT sorinpojoga unsupervisedlearningoftemporalregularitiesinvisualcorticalpopulations AT arianaandrei unsupervisedlearningoftemporalregularitiesinvisualcorticalpopulations AT valentindragoi unsupervisedlearningoftemporalregularitiesinvisualcorticalpopulations |