Topic: mouse retina

Research in our lab focuses on the circuit mechanisms underlying sensory computation. We use the mouse retina as a model system because it allows us to stimulate the circuit precisely with its natural input, patterns of light, and record its natural output, the spike trains of retinal ganglion cells. We harness the power of genetic manipulations and detailed information about cell types to uncover new circuits and discover their role in visual processing. Our methods include electrophysiology, computational modeling, and circuit tracing using a variety of imaging techniques.

To survive in the wild small rodents evolved specialized retinas. To escape predators, looming shadows need to be detected with speed and precision. To evade starvation, small seeds, grass, nuts and insects need to also be detected quickly. Some of these succulent seeds and insects may be camouflaged offering only low contrast targets.Moreover, these challenging tasks need to be accomplished continuously at dusk, night, dawn and daytime. Crossover inhibition is thought to be involved in enhancing contrast detectionin the microcircuits of the inner plexiform layer of the mammalian retina. The A

Characterizing the function of a neuron in a sensory neural network is fundamental in sensory systems neuroscience. Systems theory offers an unbiased approach to characterize the function of neural systems by their response to unstructured white noise stimuli (WN)[1,2]. Although effective in early sensory stages[3,4], it yields limited insights into neurons that perform nonlinear computations such as motion detection. Determining where the WN approach fails in the processing hierarchy constitutes an experimental question. We recorded spiking activity from mouse retinal ganglion cells (RGCs) in

Advancements in machine learning have drastically improved our ability to fit biophysical models of neural systems. We present here a fully-differentiable, biophysical model of the mouse retina that is primed for gradient-based optimization. Our model is based on the Jaxley software library (Deistler et al. 2024, Bernstein Conference) and JAX machine learning framework. The model consists of a network of Hodgkin-Huxley style neurons, realistic synapse dynamics, an experimentally-informed organization of cells in 3D space, and spatially constrained connectivity. We further implemented a wide v