Intrinsic electrical properties shape feature selectivity in a novel retinal ganglion cell

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Project Summary Neurons use their intrinsic properties to integrate and perform computations on their synaptic inputs. When considering possible mechanisms of neural computations, two primary components to consider are the network that the neuron belongs to and the intrinsic properties of the neuron itself. My proposed project uses a newly discovered retinal ganglion cell (RGC) type in mouse retina to create a clean experimental separation between network and intrinsic aspects of a sensory computation. I have identified a novel retinal ganglion cell (RGC) type: the “Bursty Suppressed-by-Contrast” (bSbC). The primary functional classification for RGC types is whether the cells are ON, OFF, ON-OFF, or the more rarely described suppressed-by-contrast (SbC). SbC RGCs suppress their firing rate from baseline to both positive and negative contrast steps. I will determine how synaptic inputs and the intrinsic properties of the bSbC support this contrast response profile. I will compare the bSbC to a well-known RGC type called the OFF sustained alpha (OFFsA) because these two cell types, despite their key functional difference, have similar morphology and similar synaptic inputs. My preliminary data suggest that differences in intrinsic properties between these two RGC types are responsible for their different contrast response functions. I will compare the intrinsic properties of the bSbC and the OFFsA using a combination of current clamp and voltage clamp recordings, single cell RNAseq, immunohistochemistry, and pharmacology. This project demonstrates that two distinct cell types in similar synaptic networks can have markedly different outputs due to intrinsic properties. I will able to dissect the contribution of channel typology and density to a specific neuronal computation. This work will deepen our understanding of retinal circuitry and, more generally, it will provide a template for dissecting the relative contribution of intrinsic properties in all neural computation.