Designing stimuli for non-invasive, cell-type-specific manipulation of retinal ganglion cell activity
- 1University of California, Los Angeles
Abstract
The retina encodes visual information through parallel pathways formed by distinct retinal ganglion (RGC) types. Current understanding of how each cell type contributes to downstream visual processing and perception is limited by a lack of tools for selectively manipulating the activities of individual RGC types. Here, we introduce a computational framework called STORM (SpatioTemporal Orthogonal Response Modulation) that creates visual stimuli that aim to selectively modulate the activities of specific RGC types while minimally affecting the activities of nearby RGCs of non-targeted types. To illustrate the approach, demonstrate its flexibility, and identify conditions under which each simulated RGC type could be selectively manipulated, we first applied this framework to simplified models of RGC function, such as Linear-Nonlinear-Poisson (LNP) and LN-LN cascade models. We also deployed this approach on more complex (and more accurate) models of RGC responses, based on convolutional neural networks. We demonstrate that differences in spatial receptive fields, temporal dynamics, response polarities, and nonlinearities can provide sufficient degrees of freedom for designing stimuli that selectively manipulate individual RGC types. We also generalize this approach to conditions where the receptive field locations are unknown for in vivo and clinical applications.
Related
- Impact of an early extended rehabilitation programme combining individually tailored nutrition and physical activity on patient outcomes after invasive mechanical ventilation and vasopressor therapy in the ICU: design and conduct of a multicentre, randomised, controlled, open-label, assessor-blinded trial (NUTRIREA-4)
- Sex-specific growth in invasive pink salmon (Oncorhynchus gorbuscha) from two regions in northern Norway
- Serotype specific anti-capsular polysaccharide IgG associated with colonizing and invasive group B streptococcus in pregnant woman-newborn dyads
- Optimizing non-invasive assessment of gastric activity: a fully automated channel-wise pipeline for multi-electrode electrogastrography
- Kidzoi Enables Cell-Type-Specific Regulatory Activity and Variant Effect Prediction in Kidney
BibTeX
@misc{ghazavi2026designing,
title = {{Designing stimuli for non-invasive, cell-type-specific manipulation of retinal ganglion cell activity}},
author = {N. Ghazavi and S. Idrees and V. M. Raval and M. B. Manookin and F. Rieke and G. D. Field and J. Zylberberg},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.30.755721},
url = {https://www.biorxiv.org/content/10.64898/2026.09.30.755721v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Designing stimuli for non-invasive, cell-type-specific manipulation of retinal ganglion cell activity
AU - N. Ghazavi
AU - S. Idrees
AU - V. M. Raval
AU - M. B. Manookin
AU - F. Rieke
AU - G. D. Field
AU - J. Zylberberg
PY - 2026
DA - 2026/09/30
PB - bioRxiv
DO - 10.64898/2026.09.30.755721
UR - https://www.biorxiv.org/content/10.64898/2026.09.30.755721v1
AB - The retina encodes visual information through parallel pathways formed by distinct retinal ganglion (RGC) types. Current understanding of how each cell type contributes to downstream visual processing and perception is limited by a lack of tools for selectively manipulating the activities of individual RGC types. Here, we introduce a computational framework called STORM (SpatioTemporal Orthogonal Response Modulation) that creates visual stimuli that aim to selectively modulate the activities of specific RGC types while minimally affecting the activities of nearby RGCs of non-targeted types. To illustrate the approach, demonstrate its flexibility, and identify conditions under which each simulated RGC type could be selectively manipulated, we first applied this framework to simplified models of RGC function, such as Linear-Nonlinear-Poisson (LNP) and LN-LN cascade models. We also deployed this approach on more complex (and more accurate) models of RGC responses, based on convolutional neural networks. We demonstrate that differences in spatial receptive fields, temporal dynamics, response polarities, and nonlinearities can provide sufficient degrees of freedom for designing stimuli that selectively manipulate individual RGC types. We also generalize this approach to conditions where the receptive field locations are unknown for in vivo and clinical applications.
ER -