Decoding natural scenes from patterned optogenetic responses in mouse visual cortex
- 1Peking University
Abstract
A central challenge in developing visual cortical prostheses is to determine how visual stimuli should be transformed into effective patterns of cortical stimulation. Although advances in stimulation technologies, including optogenetics, provide increasingly precise control over cortical activity, it remains unclear whether artificially evoked activity can reproduce the information content of naturally evoked visual representations. Here we establish a quantitative framework for evaluating visual encoding strategies by decoding cortical responses evoked by natural vision and patterned optogenetic stimulation. We developed a novel dual-modal paradigm in awake mice to bridge the gap between endogenous photostimulation and artificial network driving. By co-expressing the high-performance calcium indicator GCaMP6s and the red-shifted, ultra-sensitive opsin rsChRmine-oScarlet in the primary visual cortex (V1), we successfully translated dynamic natural movie frames into patterned, spatiotemporal optogenetic stimulation. Quantitative comparisons of macro-scale dynamics demonstrated that this patterned optogenetic injection evokes cortical states highly comparable and representationally aligned with those driven by actual visual photostimulation. To systematically evaluate the fidelity of these responses, we developed STAR, a deep learning model featuring spatial and temporal attention mechanisms, and successfully reconstructed the frames of natural movies from V1 signals under both experimental modalities. Collectively, our results demonstrate that complex sensory information can be both naturally encoded and synthetically injected into V1 circuits with high decoding fidelity. This work provides an empirical and computational proof-of-concept for intelligent, closed-loop biomimetic encoders, establishing a robust framework for next-generation cortical visual neuroprostheses and bidirectional brain-machine interfaces.
Related
- Retinal adaptive mechanisms confer selectivity to homogeneous objects in natural scenes
- Recurrent network dynamics explain the time course of perceptual grouping in natural scenes
- A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations
- Behaviorally prioritized entity-relation structure captures human visual cortical representations of natural scenes
- Edge structure influences infants' and toddlers' engagement with natural scenes.
BibTeX
@misc{jia2026decoding,
title = {{Decoding natural scenes from patterned optogenetic responses in mouse visual cortex}},
author = {S. Jia and T. Xin and C. Li and H. Ji and R. Wu and S. Wang and Q. Guo and Z. Yu and J. K. Liu and Y.-t. Li},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.21.753135},
url = {https://www.biorxiv.org/content/10.64898/2026.09.21.753135v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Decoding natural scenes from patterned optogenetic responses in mouse visual cortex
AU - S. Jia
AU - T. Xin
AU - C. Li
AU - H. Ji
AU - R. Wu
AU - S. Wang
AU - Q. Guo
AU - Z. Yu
AU - J. K. Liu
AU - Y.-t. Li
PY - 2026
DA - 2026/09/21
PB - bioRxiv
DO - 10.64898/2026.09.21.753135
UR - https://www.biorxiv.org/content/10.64898/2026.09.21.753135v1
AB - A central challenge in developing visual cortical prostheses is to determine how visual stimuli should be transformed into effective patterns of cortical stimulation. Although advances in stimulation technologies, including optogenetics, provide increasingly precise control over cortical activity, it remains unclear whether artificially evoked activity can reproduce the information content of naturally evoked visual representations. Here we establish a quantitative framework for evaluating visual encoding strategies by decoding cortical responses evoked by natural vision and patterned optogenetic stimulation. We developed a novel dual-modal paradigm in awake mice to bridge the gap between endogenous photostimulation and artificial network driving. By co-expressing the high-performance calcium indicator GCaMP6s and the red-shifted, ultra-sensitive opsin rsChRmine-oScarlet in the primary visual cortex (V1), we successfully translated dynamic natural movie frames into patterned, spatiotemporal optogenetic stimulation. Quantitative comparisons of macro-scale dynamics demonstrated that this patterned optogenetic injection evokes cortical states highly comparable and representationally aligned with those driven by actual visual photostimulation. To systematically evaluate the fidelity of these responses, we developed STAR, a deep learning model featuring spatial and temporal attention mechanisms, and successfully reconstructed the frames of natural movies from V1 signals under both experimental modalities. Collectively, our results demonstrate that complex sensory information can be both naturally encoded and synthetically injected into V1 circuits with high decoding fidelity. This work provides an empirical and computational proof-of-concept for intelligent, closed-loop biomimetic encoders, establishing a robust framework for next-generation cortical visual neuroprostheses and bidirectional brain-machine interfaces.
ER -