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The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing.

Contemporary research aims to understand biological processes not only by identifying participating proteins, but also by characterizing the dynamics of their interactions. Because Förster's Resonance Energy Transfer (FRET) is invaluable for the latter undertaking, its usage is steadily increasing. However, FRET measurements are notoriously error-prone, especially when its inherent efficiency is low, a not uncommon situation. Furthermore, many FRET methods are either difficult to implement, are not appropriate for observation of cellular dynamics, or report instrument-specific indices that hamper communication of results within the scientific community. We present here a novel comprehensive spectral methodology, SpRET, which substantially increases both the reliability and sensitivity of FRET microscopy, even under unfavorable conditions such as weak fluorescence or the presence of noise. While SpRET overcomes common pitfalls such as interchannel crosstalk and direct excitation of the acceptor, it also excels in removal of autofluorescence or background contaminations and in correcting chromatic aberrations, often overlooked factors that severely undermine FRET experiments. Finally, SpRET quantitatively reports absolute rather than relative FRET efficiency values, as well as the acceptor-to-donor molar ratio, which is critical for full and proper interpretation of FRET experiments. Thus, SpRET serves as an advanced, improved, and powerful tool in the cell biologist's toolbox.

We propose a novel approach for class-agnostic object proposal generation, which is efficient and especially well-suited to detect small objects. Efficiency is achieved by scale-specific objectness attention maps which focus the processing on promising parts of the image and reduce the amount of sampled windows strongly. This leads to a system, which is 33% faster than the state-of-the-art and clearly outperforming state-of-the-art in terms of average recall. Secondly, we add a module for detecting small objects, which are often missed by recent models. We show that this module improves the average recall for small objects by about 53%. Our implementation is available at:

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