Visual information processing occupies a substantial part of our brain. It starts in the retina, which not only converts the incoming stream of photons into electrical signals, but also performs a first analysis of the observed scene. The retina extracts information such as contrast, brightness and "color", as well as more complex stimulus features, including edges, motion and its direction. Even some initial clues about object segregation may be computed in the retina. In short, the retina is a sophisticated image processor. The retina’s processing capabilities depend on about 70 types of neurons organized in various microcircuits. Our work aims to unravel the function and organization of retinal microcircuits and to gain a better understanding of the underlying computational rules. Furthermore, we are interested in the mechanisms that implement these microcircuits during development and how microcircuits change in retinal degeneration.
Because the retina is a part of the brain that can be easily isolated and remains fully functional, in vitro, for hours, it represents an excellent model system for studying neuronal processing at both the cellular and the network level. In contrast to other parts of the brain, the in vitro retina can be provided with physiological stimuli – patterns of light – while observing the processing of these stimuli at intermediate levels in interneurons as well as at the level of the retina’s output neurons, the ganglion cells.
To study retinal processing we use a combination of electrical and optical recording techniques. The latter allows the intact, light-sensitive tissue to be visualized at a high spatial resolution and local signals to be measured in dendrites. In the retina, most dendrites are thin and entangled with each other, so that they usually cannot be accessed by recording electrodes. However, the ability to measure local dendritic activity is important for understanding neuronal processing. This is crucial for the retina, since many retinal interneurons lack dedicated output structures – such as an explicit axon – and instead use their dendrites both for receiving inputs and forming output synapses.
Learn more about our work here.
- Tom Baden, University of Sussex, Brighton, UK
- Philipp Berens, CIN / Institute of Ophthalmic Research, University of Tübingen, Germany
- Matthias Bethge, CIN / Institute for Theoretical Physics, University of Tübingen, Germany
- Karin Dedek, Dept. of Neurobiology, University of Oldenburg, Germany
- Winfried Denk, MPI of Neurobiology, Martinsried, Germany
- Silke Haverkamp, MPI for Brain Research, Frankfurt/M., Germany
- Andrew Huberman, Dept. of Neurobiology, Stanford University School of Medicine, CA, USA
- Markus Meister, Caltech, Pasadena, CA, USA
- Sebastian Seung, Princeton Neuroscience Institute and Computer Science Dept., Princeton, NJ, USA
- Robert Smith, Dept. of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
- W. Rowland Taylor, University of California Berkeley, CA, USA
- Rachel O. Wong, Dept. of Biological Structure, University of Washington, WA, USA
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