Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina

• Published in: PLoS computational biology Vol. 20; no. 9; p. e1012370
• Main Authors: Krüppel, Steffen, Khani, Mohammad H, Schreyer, Helene M, Sridhar, Shashwat, Ramakrishna, Varsha, Zapp, Sören J, Mietsch, Matthias, Karamanlis, Dimokratis, Gollisch, Tim
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.09.2024
• Subjects: Animals; Computational Biology; Computer Simulation; CT imaging; Identification and classification; Methods; Models, Neurological; Physiological aspects; Retina - diagnostic imaging; Retina - physiology; Retinal ganglion cells; Retinal Ganglion Cells - physiology; Tomography; Tomography, X-Ray Computed - methods; Germany
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1012370

Spatially nonlinear stimulus integration by retinal ganglion cells lies at the heart of various computations performed by the retina. It arises from the nonlinear transmission of signals that ganglion cells receive from bipolar cells, which thereby constitute functional subunits within a ganglion cell's receptive field. Inferring these subunits from recorded ganglion cell activity promises a new avenue for studying the functional architecture of the retina. This calls for efficient methods, which leave sufficient experimental time to leverage the acquired knowledge for further investigating identified subunits. Here, we combine concepts from super-resolution microscopy and computed tomography and introduce super-resolved tomographic reconstruction (STR) as a technique to efficiently stimulate and locate receptive field subunits. Simulations demonstrate that this approach can reliably identify subunits across a wide range of model variations, and application in recordings of primate parasol ganglion cells validates the experimental feasibility. STR can potentially reveal comprehensive subunit layouts within only a few tens of minutes of recording time, making it ideal for online analysis and closed-loop investigations of receptive field substructure in retina recordings.