Integrated Calendar

The neural network of the retina processes the stream of visual signals falling onto the eye. When a visual image is presented to the retina, retinal ganglion cells, which form the output of this network, encode changes in local visual contrast inside their receptive fields. In natural vision, however, images do not arrive in isolation, but are structured in rapid sequences, separated by frequent saccades, which activate some types of ganglion cells and suppress others. Yet, little is known about how the rapid succession of images induced by saccades affects the encoding of spatial visual information. We found that a specific type of retinal ganglion cells, recorded in mouse retina, displays unexpected responses to saccade-like image transitions; the cells elicit a distinct spike burst when the same visual pattern reappears after the transition, providing a special code for such transitions or image parts that lead to a recurrence of stimulus patterns. This sensitivity to image recurrence is mediated by a circuit of serial inhibition, allowing a rapid reappearance of the image to suppress transition-induced inhibition of the ganglion cell. Our results show that saccade-like image transitions trigger interactions in the complex inhibitory network of the retina that lead to a dynamical gating of the information flow through the retina and provide a mode of operation that differs from the processing of simple, standard laboratory stimuli.

The origin and composition of 85% of the matter in the universe is completely unknown. Among several viable options, Weakly Interacting Massive Particles (WIMPs) are motivated dark matter candidates that can be tested by different and complementary search strategies. Crucially, different searches probe WIMP couplings at different energy scales, and such a separation of scales has striking consequences in connecting different experimental probes. This motivates the development of theoretical tools to properly connect the different energy scales involved in constraining WIMP models. I will introduce these tools and I will illustrate with several examples how crucial the inclusion of these effects in WIMP searches is.