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How we perceive a visual stimulus can be influenced by its surrounding context. For example, the presence of a reference skews the perception of a similar feature in a stimulus, a phenomenon called reference repulsion. Ongoing research so far remains inconclusive regarding the stage of visual information processing where such repulsion occurs. We examined the influence of a reference on late visual processing. We measured the repulsion effect caused by an orientation reference presented after an orientation ensemble stimulus. The participants’ reported orientations were significantly biased away from the post-stimulus reference, displaying typical characteristics of reference repulsion. Moreover, explicit discrimination choices between the reference and the stimulus influenced the magnitudes of repulsion effects, which can be explained by an encoding-decoding model that differentiates the re-weighting of sensory representations in implicit and explicit processes. These results support the notion that reference repulsion may arise at a late decision-related stage of visual processing, where different sensory decoding strategies are employed depending on the specific task.

Among the five photoreceptor opsins in the eye of Drosophila, Rhodopsin 1 (Rh1) is expressed in the six outer photoreceptors. In a previous study that combined behavioral genetics with computational modeling, we demonstrated that flies can use the signals from Rh1 for color vision. Here, we provide an in-depth computational analysis of wildtype Drosophila wavelength discrimination specifically considering the consequences of different choices of computations in the preprocessing of the behavioral data. The results support the conclusion that Drosophila wavelength discrimination behavior can best be explained by a contribution of Rh1. These findings are corroborated by results of an information-theoretical analysis that shows that Rh1 provides information for discrimination of natural reflectance spectra.

The CoCoMac database contains the results of several hundred published axonal tract-tracing studies in the macaque monkey brain. The combined results are used for constructing the macaque macro-connectome. Here we discuss the redevelopment of CoCoMac and compare it to six connectome-related projects: two online resources that provide full access to raw tracing data in rodents, a connectome viewer for advanced 3D graphics, a partial but highly detailed rat connectome, a brain data management system that generates custom connectivity matrices, and a software package that covers the complete pipeline from connectivity data to large-scale brain simulations. The second edition of CoCoMac features many enhancements over the original. For example, a search wizard is provided for full access to all tables and their nested dependencies. Connectivity matrices can be computed on demand in a user-selected nomenclature. A new data entry system is available as a preview, and is to become a generic solution for community-driven data entry in manually collated databases. We conclude with the question whether neuronal tracing will remain the gold standard to uncover the wiring of brains, thereby highlighting developments in human connectome construction, tracer substances, polarized light imaging, and serial block-face scanning electron microscopy.

Background Morphological features are widely used in the study of neuronal function and pathology. Invertebrate neurons are often structurally stereotypical, showing little variance in gross spatial features but larger variance in their fine features. Such variability can be quantified using detailed spatial analysis, which however requires the morphologies to be registered to a common frame of reference. Results We outline here new algorithms — Reg-MaxS and Reg-MaxS-N — for co-registering pairs and groups of morphologies, respectively. Reg-MaxS applies a sequence of translation, rotation and scaling transformations, estimating at each step the transformation parameters that maximize spatial overlap between the volumes occupied by the morphologies. We test this algorithm with synthetic morphologies, showing that it can account for a wide range of transformation differences and is robust to noise. Reg-MaxS-N co-registers groups of more than two morphologies by iteratively calculating an average volume and registering all morphologies to this average using Reg-MaxS. We test Reg-MaxS-N using five groups of morphologies from the Droshophila melanogaster brain and identify the cases for which it outperforms existing algorithms and produce morphologies very similar to those obtained from registration to a standard brain atlas. Conclusions We have described and tested algorithms for co-registering pairs and groups of neuron morphologies. We have demonstrated their application to spatial comparison of stereotypic morphologies and calculation of dendritic density profiles, showing how our algorithms for registering neuron morphologies can enable new approaches in comparative morphological analyses and visualization.

Objective: We are still lacking a pathophysiological mechanism for functional disorders explaining the emergence and manifestation of characteristic, severely impairing bodily symptoms like chest pain or dizziness. A recent hypothesis based on the predictive coding theory of brain function suggests that in functional disorders, internal expectations do not match the actual sensory body states, leading to perceptual dysregulation and symptom perception. To test this hypothesis, we investigated the account of internal expectations and sensory input on gaze stabilization, a physiologically relevant parameter of gaze shifts, in functional dizziness. Methods: We assessed gaze stabilization in eight functional dizziness patients and 11 healthy controls during two distinct epochs of large gaze shifts: during a counter-rotation epoch (CR epoch), where the brain can use internal models, motor planning, and resulting internal expectations to achieve internally driven gaze stabilization; and during an oscillation epoch (OSC epoch), where, due to terminated motor planning, no movement expectations are present, and gaze is stabilized by sensory input alone. Results: Gaze stabilization differed between functional patients and healthy controls only when internal movement expectations were involved [ F (1,17) = 14.63, p = 0.001, and partial η 2 = 0.463]: functional dizziness patients showed reduced gaze stabilization during the CR ( p = 0.036) but not OSC epoch ( p = 0.26). Conclusion: While sensory-driven gaze stabilization is intact, there are marked, well-measurable deficits in internally-driven gaze stabilization in functional dizziness pointing at internal expectations that do not match actual body states. This experimental evidence supports the perceptual dysregulation hypothesis of functional disorders and is an important step toward understanding the underlying pathophysiology.

The morphology of a neuron is strongly related to its physiological properties, application of logical product and thus to information processing functions. Optical microscope images are widely used for extracting the structure of neurons. Although several approaches have been proposed to trace and extract complex neuronal structures from microscopy images, available methods remain prone to errors. In this study, we present a practical scheme for processing confocal microscope images and reconstructing neuronal structures. We evaluated this scheme using image data samples and associated \"gold standard\" reconstructions from the BigNeuron Project. In these samples, dendritic arbors belonging to multiple projection branches of the same neuron overlapped in space, making it difficult to automatically and accurately trace their structural connectivity. Our proposed scheme, which combines several software tools for image masking and filtering with an existing tool for dendritic segmentation and tracing, outperformed state-of-the-art automatic methods in reconstructing such neuron structures. For evaluating our scheme, we applied it to a honeybee local interneuron, DL-Int-1, which has complex arbors and is considered to be a critical neuron for encoding the distance information indicated in the waggle dance of the honeybee.

Sharing of data, processing tools, and workflows require open data hosting services and management tools. Despite FAIR guidelines and the increasing demand from funding agencies and publishers, only a few animal studies share all experimental data and processing tools. We present a step-by-step protocol to perform version control and remote collaboration for large multimodal datasets. A data management plan was introduced to ensure data security in addition to a homogeneous file and folder structure. Changes to the data were automatically tracked using DataLad and all data was shared on the research data platform GIN. This simple and cost-effective workflow facilitates the adoption of FAIR data logistics and processing workflows by making the raw and processed data available and providing the technical infrastructure to independently reproduce the data processing steps. It enables the community to collect heterogeneously acquired and stored datasets not limited to a specific category of data and serves as a technical infrastructure blueprint with rich potential to improve data handling at other sites and extend to other research areas.

Metadata providing information about the stimulus, data acquisition, and experimental conditions are indispensable for the analysis and management of experimental data within a lab. However, only rarely are metadata available in a structured, comprehensive, and machine-readable form. This poses a severe problem for finding and retrieving data, both in the laboratory and on the various emerging public data bases. Here, we propose a simple format, the \"open metaData Markup Language\" (odML), for collecting and exchanging metadata in an automated, computer-based fashion. In odML arbitrary metadata information is stored as extended key-value pairs in a hierarchical structure. Central to odML is a clear separation of format and content, i.e., neither keys nor values are defined by the format. This makes odML flexible enough for storing all available metadata instantly without the necessity to submit new keys to an ontology or controlled terminology. Common standard keys can be defined in odML-terminologies for guaranteeing interoperability. We started to define such terminologies for neurophysiological data, but aim at a community driven extension and refinement of the proposed definitions. By customized terminologies that map to these standard terminologies, metadata can be named and organized as required or preferred without softening the standard. Together with the respective libraries provided for common programming languages, the odML format can be integrated into the laboratory workflow, facilitating automated collection of metadata information where it becomes available. The flexibility of odML also encourages a community driven collection and definition of terms used for annotating data in the neurosciences.

An essential aspect of scientific reproducibility is a coherent and complete acquisition of metadata along with the actual data of an experiment. The high degree of complexity and heterogeneity of neuroscience experiments requires a rigorous management of the associated metadata. The odML framework represents a solution to organize and store complex metadata digitally in a hierarchical format that is both human and machine readable. However, this hierarchical representation of metadata is difficult to handle when metadata entries need to be collected and edited manually during the daily routines of a laboratory. With odMLtables, we present an open-source software solution that enables users to collect, manipulate, visualize, and store metadata in tabular representations (in [xls]{} or [csv]{} format) by providing functionality to convert these tabular collections to the hierarchically structured metadata format odML, and to either extract or merge subsets of a complex metadata collection. With this, odMLtables bridges the gap between handling metadata in an intuitive way that integrates well with daily lab routines and commonly used software products on the one hand, and the implementation of a complete, well-defined metadata collection for the experiment in a standardized format on the other hand. We demonstrate usage scenarios of the odMLtables tools in common lab routines in the context of metadata acquisition and management, and show how the tool can assist in exploring published datasets that provide metadata in the odML format.

To date, non-reproducibility of neurophysiological research is a matter of intense discussion in the scientific community. A crucial component to enhance reproducibility is to comprehensively collect and store metadata, that is, all information about the experiment, the data, and the applied preprocessing steps on the data, such that they can be accessed and shared in a consistent and simple manner. However, the complexity of experiments, the highly specialized analysis workflows and a lack of knowledge on how to make use of supporting software tools often overburden researchers to perform such a detailed documentation. For this reason, the collected metadata are often incomplete, incomprehensible for outsiders or ambiguous. Based on our research experience in dealing with diverse datasets, we here provide conceptual and technical guidance to overcome the challenges associated with the collection, organization, and storage of metadata in a neurophysiology laboratory. Through the concrete example of managing the metadata of a complex experiment that yields multi-channel recordings from monkeys performing a behavioral motor task, we practically demonstrate the implementation of these approaches and solutions with the intention that they may be generalized to other projects. Moreover, we detail five use cases that demonstrate the resulting benefits of constructing a well-organized metadata collection when processing or analyzing the recorded data, in particular when these are shared between laboratories in a modern scientific collaboration. Finally, we suggest an adaptable workflow to accumulate, structure and store metadata from different sources using, by way of example, the odML metadata framework.