Biophysical diversity in retinal ganglion cells

Neuronal signal transmission in living systems is mostly realized via repetitive generation of so- called action potentials. An action potential is characterized by a fast de- and repolarization of the cellular membrane potential an all-or-none principle - which gets propagated along nerve fibers. Trains of action potentials build the neuronal code of the nervous system. In between nerve cells synapses convert action potentials from an input cell into analog signals and pass them to an output cell which again generates one or multiple action potentials. In the classical view, neuronal output is determined by the input a cell receives. However, the intrinsic, biophysical properties of nerve cells that shape neuronal function are quite under- explored. The capability of generating action potentials is variable in different types of nerve cells. While some cells are able to create long duration, high-frequency trains of action potentials, others can only generate single action potentials to sustained input. The biophysical properties of nerve cells that allow action potential generation are not well understood, especially whether these properties are correlated to the received inputs. Therefore, this project aims to reveal i) the cellular properties that shape the specific output of single nerve cells in the retina; and ii) whether the cellular properties are optimized to the synaptic inputs these cells receive. The project will make use of multiple experimental techniques that allow us to measure the response of single mouse retinal ganglion cells. We will also investigate the anatomy of retinal ganglion cells and how it contributes to the observed response patterns. Detailed computer simulations will support our analysis and will enable us to study the influence of single features of retinal ganglion cells on elicited responses. Potential findings of this project are not specific to retinal ganglion cells as all nerve cells follow the same all-or-none principle. Therefore, our findings will have broad implications on neuroscience in general and our understanding of neuronal signal transmission.

A central pillar of this research project is the discovery that retinal ganglion cells (RGCs), the key output neurons of the retina, maintain their distinct intrinsic firing identities even when upstream photoreceptors, cells that convert light to neuronal signals, have degenerated. Project results show that RGCs each possess unique internal "spike-generating" properties that remain stable, despite the loss of normal visual input. These intrinsic differences in how each cell type initiates and shapes electrical signals mirror the roles they play in healthy vision, suggesting that the retina's output layer preserves a robust functional blueprint. This finding is crucial: it means that even in advanced disease, the retina retains meaningful cellular structure that artificial vision systems can leverage. Instead of trying to recreate the entire visual pathway, future prosthetic technologies can directly engage these preserved cell types, respecting the natural division of labor built into the retina. Results also show that RGCs can be clustered based on their spiking properties. While RGCs are mainly classified via visual stimulation we show that also their intrinsic properties differ from each other and these differences might be tuned to synaptic inputs. Building on this biological foundation, companion work explores how to stimulate these RGCs with far greater precision which is an essential step toward high-resolution retinal prostheses. Several studies demonstrate that both ultra-short electrical pulses and low-frequency sinusoidal waveforms can activate RGC cell bodies without triggering action potentials in axons, reducing the spread of unwanted activation that has long blurred the "image" produced by current implants. Together, these stimulation innovations translate the biological insight, stable, identifiable RGC types, into practical strategies for addressing each type with tailored signals. By combining an understanding of how RGCs intrinsically behave with technologies that can selectively engage them, this body of work points toward retinal prostheses capable not only of restoring vision, but of respecting and harnessing the retina's natural computational architecture.