Receptive Field Vectors of Genetically-Identified Retinal Ganglion Cells Reveal Cell-Type-Dependent Visual Functions

• Published in: PloS one Vol. 11; no. 2; p. e0147738
• Main Authors: Katz, Matthew L., Viney, Tim J., Nikolic, Konstantin
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.02.2016; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Biology and Life Sciences; Biomedical engineering; Biotin - analogs & derivatives; Black spot; Coding; Data processing; Engineering; Entropy; Firing pattern; Gene Expression; Genes, Reporter; Genetic aspects; Medicine and Health Sciences; Memory; Methods; Mice; Mice, Transgenic; Neurons; Neurosciences; Parvalbumins - genetics; Photic Stimulation; Physical Sciences; Physiological aspects; Receptive field; Retina; Retinal ganglion cells; Retinal Ganglion Cells - physiology; Sensory evaluation; Sensory stimuli; Social Sciences; Somatosensory evoked potentials; Transgenic mice; Vision, Ocular; Visual Fields; Visual stimuli; Switzerland; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0147738

Sensory stimuli are encoded by diverse kinds of neurons but the identities of the recorded neurons that are studied are often unknown. We explored in detail the firing patterns of eight previously defined genetically-identified retinal ganglion cell (RGC) types from a single transgenic mouse line. We first introduce a new technique of deriving receptive field vectors (RFVs) which utilises a modified form of mutual information ("Quadratic Mutual Information"). We analysed the firing patterns of RGCs during presentation of short duration (~10 second) complex visual scenes (natural movies). We probed the high dimensional space formed by the visual input for a much smaller dimensional subspace of RFVs that give the most information about the response of each cell. The new technique is very efficient and fast and the derivation of novel types of RFVs formed by the natural scene visual input was possible even with limited numbers of spikes per cell. This approach enabled us to estimate the 'visual memory' of each cell type and the corresponding receptive field area by calculating Mutual Information as a function of the number of frames and radius. Finally, we made predictions of biologically relevant functions based on the RFVs of each cell type. RGC class analysis was complemented with results for the cells' response to simple visual input in the form of black and white spot stimulation, and their classification on several key physiological metrics. Thus RFVs lead to predictions of biological roles based on limited data and facilitate analysis of sensory-evoked spiking data from defined cell types.