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Peer-reviewedCC BY 4.0eLife

Dual-feature selectivity enables bidirectional coding in visual cortical neurons

  • Nikos Karantzas1,2,3
  • Katrin Franke1,2,3,4
  • Konstantin Friedrich Willeke1,2,3
  • Maria Diamantaki5,6
  • Kandan Ramakrishnan7
  • Hasan Atakan Bedel1,2,3
  • Pavithra Elumalai8
  • Kelli Restivo7
  • Paul Fahey1,2,3
  • Cate Nealley1,2,3
  • Tori Shinn9
  • Gabby Garcia1,2,3
7 more authors
  • Saumil Patel1,2,3
  • Alexander Ecker8,10,11
  • Edgar Walker12,13
  • Emmanouil Froudarakis5,6
  • Sophia Sanborn1,2,3
  • Fabian H Sinz7,8,10
  • Andreas Savas Tolias1,2,3
13 affiliations
  1. 1Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, United States
  2. 2Stanford Bio-X, Stanford University, Stanford, United States
  3. 3Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States
  4. 4Institute for Ophthalmic Research, Tübingen University, Tübingen, Germany
  5. 5Institute of Molecular Biology and Biotechnology, Foundation of Research and Technology- Hellas, Heraklion, Greece
  6. 6Department of Basic Sciences, Faculty of Medicine, University of Crete, Heraklion, Greece
  7. 7Department of Neuroscience and Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, United States
  8. 8Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, Germany
  9. 9Department of Pediatrics; Allergy and Immunology, Baylor College of Medicine, Houston, United States
  10. 10Lower Saxony Center for AI and Causal Methods in Medicine, Hannover, Germany
  11. 11Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
  12. 12Department of Neurobiology and Biophysics, University of Washington School of Medicine, Seattle, United States
  13. 13Computational Neuroscience Center, University of Washington, Seattle, United States
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Abstract

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features—one that enhances and one that suppresses activity—around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models—deep learning-based predictive models of biological neurons—we were able to systematically identify each neuron’s preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

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Cite

BibTeXRIS
BibTeX
@article{karantzas2026dualfeature,
  title = {{Dual-feature selectivity enables bidirectional coding in visual cortical neurons}},
  author = {Nikos Karantzas and Katrin Franke and Konstantin Friedrich Willeke and Maria Diamantaki and Kandan Ramakrishnan and Hasan Atakan Bedel and Pavithra Elumalai and Kelli Restivo and Paul Fahey and Cate Nealley and Tori Shinn and Gabby Garcia and Saumil Patel and Alexander Ecker and Edgar Walker and Emmanouil Froudarakis and Sophia Sanborn and Fabian H Sinz and Andreas Savas Tolias},
  journal = {eLife},
  year = {2026},
  month = nov,
  doi = {10.7554/elife.109861},
  url = {https://elifesciences.org/articles/109861},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Dual-feature selectivity enables bidirectional coding in visual cortical neurons
AU  - Nikos Karantzas
AU  - Katrin Franke
AU  - Konstantin Friedrich Willeke
AU  - Maria Diamantaki
AU  - Kandan Ramakrishnan
AU  - Hasan Atakan Bedel
AU  - Pavithra Elumalai
AU  - Kelli Restivo
AU  - Paul Fahey
AU  - Cate Nealley
AU  - Tori Shinn
AU  - Gabby Garcia
AU  - Saumil Patel
AU  - Alexander Ecker
AU  - Edgar Walker
AU  - Emmanouil Froudarakis
AU  - Sophia Sanborn
AU  - Fabian H Sinz
AU  - Andreas Savas Tolias
PY  - 2026
DA  - 2026/11/09
JO  - eLife
DO  - 10.7554/elife.109861
UR  - https://elifesciences.org/articles/109861
AB  - Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features—one that enhances and one that suppresses activity—around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models—deep learning-based predictive models of biological neurons—we were able to systematically identify each neuron’s preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.
SN  - 2050-084X
ER  -