Perceptual decision making is an active process where animals move their sense organs to extract task-relevant information. To investigate how the brain translates sensory input into decisions during active sensation, we developed a mouse active touch task where the mechanosensory input can be precisely measured and that challenges animals to use multiple mechanosensory cues. Male mice were trained to localise a pole using a single whisker and to report their decision by selecting one of three choices. Using high-speed imaging and machine vision we estimated whisker-object mechanical forces at millisecond resolution. Mice solved the task by a sensory-motor strategy where both the strength and direction of whisker bending were informative cues to pole location. We found competing influences of immediate sensory input and choice memory on mouse choice. On correct trials, choice could be predicted from the direction and strength of whisker bending, but not from previous choice. In contrast, on error trials, choice could be predicted from previous choice but not from whisker bending. This study shows that animal choices during active tactile decision making can be predicted from mechanosenory and choice-memory signals; and provides a new task, well-suited for future study of the neural basis of active perceptual decisions.Due to the difficulty of measuring the sensory input to moving sense organs, active perceptual decision making remains poorly understood. The whisker system provides a way forward since it is now possible to measure the mechanical forces due to whisker-object contact during behaviour. Here we train mice in a novel behavioural task that challenges them to use rich mechanosensory cues, but can be performed using one whisker and enables task-relevant mechanical forces to be precisely estimated. This approach enables rigorous study of how sensory cues translate into action during active, perceptual decision making. Our findings provide new insight into active touch and how sensory/internal signals interact to determine behavioural choices.

A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST's binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.

To explore theories of predictive coding, we presented mice with repeated sequences of images with novel images sparsely substituted. Under these conditions, mice could be rapidly trained to lick in response to a novel image, demonstrating a high level of performance on the first day of testing. Using 2-photon calcium imaging to record from layer 2/3 neurons in the primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. When a new stimulus sequence was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which then decayed to almost zero activity. The decay time of these transient responses was not fixed, but instead scaled with the length of the stimulus sequence. However, at the same time, we also found a small fraction of the neurons within the population (\~2%) that continued to respond strongly and periodically to the repeated stimulus. Decoding analysis demonstrated that both the transient and sustained responses encoded information about stimulus identity. We conclude that the layer 2/3 population uses a two-channel predictive code: a dense transient code for novel stimuli and a sparse sustained code for familiar stimuli. These results extend and unify existing theories about the nature of predictive neural codes.

Alcohol addiction is a common affliction with a strong genetic component [1]. Although mammalian studies have provided significant insight into the molecular mechanisms underlying ethanol consumption [2], other organisms such as Drosophila melanogaster are better suited for unbiased, forward genetic approaches to identify novel genes. Behavioral responses to ethanol, such as hyperactivity, sedation, and tolerance, are conserved between flies and mammals [3, 4], as are the underlying molecular pathways [5-9]. However, few studies have investigated ethanol self-administration in flies [10]. Here we characterize ethanol consumption and preference in Drosophila. Flies prefer to consume ethanol-containing food over regular food, and this preference increases over time. Flies are attracted to the smell of ethanol, which partially mediates ethanol preference, but are averse to its taste. Preference for consuming ethanol is not entirely explained by attraction to either its sensory or caloric properties. We demonstrate that flies can exhibit features of alcohol addiction. First, flies self-administer ethanol to pharmacologically relevant concentrations. Second, flies will overcome an aversive stimulus in order to consume ethanol. Third, flies rapidly return to high levels of ethanol consumption after a period of imposed abstinence. Thus, ethanol preference in Drosophila provides a new model for studying aspects of addiction.

Motivation: A significant focus of biological research is to understand the development, organization and function of tissues. A particularly productive area of study is on single layer epithelial tissues in which the adherence junctions of cells form a 2D manifold that is fluorescently labeled. Given the size of the tissue, a microscope must collect a mosaic of overlapping 3D stacks encompassing the stained surface. Downstream interpretation is greatly simplified by preprocessing such a dataset as follows: (a) extracting and mapping the stained manifold in each stack into a single 2D projection plane, (b) correcting uneven illumination artifacts, (c) stitching the mosaic planes into a single, large 2D image, and (d) adjusting the contrast.

Results: We have developed PreMosa, an efficient, fully automatic pipeline to perform the four preprocessing tasks above resulting in a single 2D image of the stained manifold across which contrast is optimized and illumination is even. Notable features are as follows. First, the 2D projection step employs a specially developed algorithm that actually finds the manifold in the stack based on maximizing contrast, intensity and smoothness. Second, the projection step comes first, implying all subsequent tasks are more rapidly solved in 2D. And last, the mosaic melding employs an algorithm that globally adjusts contrasts amongst the 2D tiles so as to produce a seamless, high-contrast image. We conclude with an evaluation using ground-truth datasets and present results on datasets from Drosophila melanogaster wings and Schmidtae mediterranea ciliary components.

Availability: PreMosa is available under https://cblasse.github.io/premosa.

Contact: blasse@mpi-cbg.de, myers@mpi-cbg.de.

Induced pluripotent stem cell (iPSC)-based models are powerful tools to study neurodegenerative diseases such as Parkinson's disease. The differentiation of patient-derived neurons and astrocytes allows investigation of the molecular mechanisms responsible for disease onset and development. In particular, these two cell types can be mono- or co-cultured to study the influence of cell-autonomous and non-cell-autonomous contributors to neurodegenerative diseases. We developed a streamlined procedure to produce high-quality/high-purity cultures of dopaminergic neurons and astrocytes that originate from the same population of midbrain floor-plate progenitors. This unit describes differentiation, quality control, culture parameters, and troubleshooting tips to ensure the highest quality and reproducibility of research results. © 2019 The Authors. Basic Protocol 1: Differentiation of iPSCs into midbrain-patterned neural progenitor cells Support Protocol: Quality control of neural progenitor cells Basic Protocol 2: Differentiation of neural progenitor cells into astrocytes Basic Protocol 3: Differentiation of neural progenitor cells into dopaminergic neurons Basic Protocol 4: Co-culture of iPSC-derived neurons and astrocytes.

The Drosophila cuticle carries a rich array of morphological details. Thus, cuticle examination has had a central role in the history of genetics. To prepare fine "museum-quality," permanent slides, it is best to mount specimens in Canada Balsam. It is difficult to give precise recipes for Canada Balsam, because every user seems to prefer a slightly different viscosity. Dilute solutions spread easily and do not dry too rapidly while mounting specimens. The disadvantage is that there is actually less Balsam in a "drop" of the solution, and when dried, it can contract from the sides of the coverslip, sometimes disturbing the specimen. Unfortunately, there is no substitute for experience when using Canada Balsam. This protocol describes a procedure for mounting adult cuticles in Canada Balsam.

The Drosophila cuticle carries a rich array of morphological details. Thus, cuticle examination has had a central role in the history of genetics. Studies of the Drosophila cuticle have focused mainly on first-instar larvae and adult cuticular morphology. Although the cuticles of second- and third-instar larvae are strikingly different from those of the first instar, these differences have been poorly studied. This protocol describes three methods for preparing cuticles from fed larvae. One commonly used procedure involves manually pricking the larvae. A simpler method for preparing larval cuticles is to burst the larvae once they have been mounted. This method is used for first- and second-instar larvae and does not require pricking; it removes the gut contents by "popping" the rear of the embryo using pressure from the coverslip. If just the right amount of medium is used, the coverslip will be pulled toward the slide, applying pressure on the samples. The larvae usually burst from their posterior ends. Also presented is an alternative procedure designed specifically for the use with third-instar larvae, although the "pricking" method can be used at this stage.

The finely sculpted cuticle of Drosophila carries a rich array of morphological details. Thus, cuticle examination has had a central role in the history of genetics. Studies of the Drosophila cuticle have focused mainly on first-instar larvae and adult cuticular morphology. This protocol describes the preparation of cuticles from larvae that have not yet hatched from the egg. It is designed for sampling all eggs laid by one or more females. This can be particularly useful, for example, when a mutation produces embryos that are unable to hatch from the egg.

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals because of their common evolutionarily specified developmental programme. Such organization at the circuit level may constrain neural activity, leading to low-dimensional latent dynamics across the neural population. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.