Using Biologically-inspired Image Features to Model Retinal Response: Evidence from Biological Datasets
- Resource Type
- Conference
- Authors
- Melanitis, Nikos; Nakopoulos, Giorgos; Lozano, Antonio; Soto-Sanchez, Cristina; Fernandez, Eduardo; Nikita, Konstantina S.
- Source
- 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Engineering in Medicine & Biology Society (EMBC), 2021 43rd Annual International Conference of the IEEE. :3378-3381 Nov, 2021
- Subject
- Bioengineering
Analytical models
Visualization
Biological system modeling
Retina
Feature extraction
Brain modeling
Data models
retinal model
bio-inspired features
computer vision
retinal prosthesis
biological data
- Language
- ISSN
- 2694-0604
Retinal models are needed to simulate the translation of visual percepts to Retinal Ganglion Cells (RGCs) neural spike trains, through which visual information is transmitted to the brain. Restoring vision through neural prostheses motivates the development of accurate retinal models. We integrate biologically-inspired image features to RGC models. We trained Linear-Nonlinear models using response data from biological retinae. We show that augmenting raw image input with retina-inspired image features leads to performance improvements: in a smaller (30sec. of retina recordings) set integration of features leads to improved models in approximately $\frac{2}{3}$ of the modeled RGCS; in a larger (4min. recording) we show that utilizing Spike Triggered Average analysis to localize RGCs in input images and extract features in a cell-based manner leads to improved models in all (except two) of the modeled RGCs.