Inference of action potentials (‘spikes’) from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals (‘ground truth’). We compiled a large, diverse ground-truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types, and signal-to-noise ratios, comprising a total of >35 recording hours from 298 neurons. We developed an algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground-truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground-truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly release a resource toolbox, and provide a user-friendly cloud-based implementation.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Sensory systems balance stability and plasticity to optimize stimulus representations in dynamic environments. We studied these processes in the olfactory system of adult zebrafish. Activity patterns evoked by repeated odor stimulation were measured by multiphoton calcium imaging in the olfactory bulb (OB) and in telencephalic area Dp, the homolog of olfactory cortex. Whereas odor responses in the OB were highly reproducible, responses of Dp neurons adapted over trials and exhibited substantial variability that could be attributed to ongoing activity and to systematic changes in neuronal representations following each stimulus. An NMDA receptor antagonist did not affect the magnitude of odor responses but strongly reduced the variability and experience-dependent modification of odor responses in Dp. As a consequence, odor representations became stable over trials. These results demonstrate that odor representations in higher brain areas are continuously modified by experience, supporting the view that olfactory processing is inseparable from memory, even in the absence of reinforcement.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations 1, 2 . However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators 3 . Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past 4-8 , but it is an open question how far performance can be improved. Here, we report the results of the spikefinder 2 . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/177956 doi: bioRxiv preprint first posted online Aug. 18, 2017; challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.We organized the spikefinder challenge to crowd-source the problem of developing algorithms for inferring spike rates from calcium signals. Such challenges have been used successfully in machine learning and computer vision to drive algorithmic developments 9 . We used a benchmark data set consisting of 92 recordings from 73 neurons 6 , acquired in the primary visual cortex and the retina of mice using different calcium indicators (OGB-1 and GCaMP6s), different scanning methods, and different sampling rates. For this data, calcium imaging has been performed simultaneously with electrophysiological recordings allowing to evaluate the performance of spike inference algorithms on ground truth data. Importantly, all data has been acquired at a zoom factor typically used during population imaging experiments, ensuring that all benchmark results reflect performance under the typical use-case conditions.For the challenge, we split the data into a training and a test set (see Table 1). For the training data, we made both the calcium and the spike traces publicly available, but kept the spike traces secret for the test data. Additionally, the publicly available data sets provided by the GENIE project 10 were available as training data. This allowed participants to adjust their models on the 3 . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/177956 doi: bioRxiv preprint first posted online Aug. 18, 2017; training data set, while avoiding overfitting to the specific benchmark data set. Participants could upload predictions for the spike rate generated by their algorithm on a dedicate...

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There is a high demand for 3D multiphoton imaging in neuroscience and other fields but scanning in axial direction presents technical challenges. We developed a focusing technique based on a remote movable mirror that is conjugate to the specimen plane and translated by a voice coil motor. We constructed cost-effective z-scanning modules from off-the-shelf components that can be mounted onto standard multiphoton laser scanning microscopes to extend scan patterns from 2D to 3D. Systems were designed for large objectives and provide high resolution, high speed and a large z-scan range (>300 μm). We used these systems for 3D multiphoton calcium imaging in the adult zebrafish brain and measured odor-evoked activity patterns across >1500 neurons with single-neuron resolution and high signal-to-noise ratio.

Virtual realities are powerful tools to analyze and manipulate interactions between animals and their environment and to enable measurements of neuronal activity during behavior. In many species, however, optical access to the brain and/or the behavioral repertoire are limited. We developed a high-resolution virtual reality for head-restrained adult zebrafish, which exhibit cognitive behaviors not shown by larvae. We noninvasively measured activity throughout the dorsal telencephalon by multiphoton calcium imaging. Fish in the virtual reality showed regular swimming patterns and were attracted to animations of conspecifics. Manipulations of visuo-motor feedback revealed neurons that responded selectively to the mismatch between the expected and the actual visual consequences of motor output. Such error signals were prominent in multiple telencephalic areas, consistent with models of predictive processing. A virtual reality system for adult zebrafish therefore provides opportunities to analyze neuronal processing mechanisms underlying higher brain functions including decision making, associative learning, and social interactions.

We have developed a method for studying cellular adhesion by using a custom-designed microfluidic device with parallel non-connected tapered channels. The design enables investigation of cellular responses to a large range of shear stress (ratio of 25) with a single input flow-rate. For each shear stress, a large number of cells are analyzed (500-1500 cells), providing statistically relevant data within a single experiment. Besides adhesion strength measurements, the microsystem presented in this paper enables in-depth analysis of cell detachment kinetics by real-time videomicroscopy. It offers the possibility to analyze adhesion-associated processes, such as migration or cell shape change, within the same experiment. To show the versatility of our device, we examined quantitatively cell adhesion by analyzing kinetics, adhesive strength and migration behaviour or cell shape modifications of the unicellular model cell organism Dictyostelium discoideum at 21 °C and of the human breast cancer cell line MDA-MB-231 at 37 °C. For both cell types, we found that the threshold stresses, which are necessary to detach the cells, follow lognormal distributions, and that the detachment process follows first order kinetics. In addition, for particular conditions' cells are found to exhibit similar adhesion threshold stresses, but very different detachment kinetics, revealing the importance of dynamics analysis to fully describe cell adhesion. With its rapid implementation and potential for parallel sample processing, such microsystem offers a highly controllable platform for exploring cell adhesion characteristics in a large set of environmental conditions and cell types, and could have wide applications across cell biology, tissue engineering, and cell screening.