Spyke Viewer: a flexible and extensible platform for electrophysiological data analysis

Spyke Viewer is an open source application designed to help researchers analyze data from electrophysiological recordings or neural simulations. It provides a graphical data browser and supports finding and selecting relevant subsets of the data. Users can interact with the selected data using an integrated Python console or plugins. Spyke Viewer includes plugins for several common visualizations and allows users to easily extend the program by writing their own plugins. New plugins are automatically integrated with the graphical interface. Additional plugins can be downloaded and shared on a dedicated website.DFG, GRK 1589, Verarbeitung sensorischer Informationen in neuronalen Systeme

Structure of sulfamidase provides insight into the molecular pathology of mucopolysaccharidosis IIIA

Mucopolysaccharidosis type IIIA (Sanfilippo A syndrome), a fatal childhood-onset neurodegenerative disease with mild facial, visceral and skeletal abnormalities, is caused by an inherited deficiency of the enzyme N-sulfoglucosamine sulfohydrolase (SGSH; sulfamidase). More than 100 mutations in the SGSH gene have been found to reduce or eliminate its enzymatic activity. However, the molecular understanding of the effect of these mutations has been confined by a lack of structural data for this enzyme. Here, the crystal structure of glycosylated SGSH is presented at 2Å resolution. Despite the low sequence identity between this unique N-sulfatase and the group of O-sulfatases, they share a similar overall fold and active-site architecture, including a catalytic formylglycine, a divalent metal-binding site and a sulfate-binding site. However, a highly conserved lysine in O-sulfatases is replaced in SGSH by an arginine (Arg282) that is positioned to bind the N-linked sulfate substrate. The structure also provides insight into the diverse effects of pathogenic mutations on SGSH function in mucopolysaccharidosis type IIIA and convincing evidence for the molecular consequences of many missense mutations. Further, the molecular characterization of SGSH mutations will lay the groundwork for the development of structure-based drug design for this devastating neurodegenerative disorder. © 2014 International Union of Crystallography.This work was funded by the DFG. Partial support from DFG grant No. SH 14/5-1 is gratefully acknowledged (NSS). IU is grateful to the Spanish MEC and Generalitat de Catalunya for financial support (grants BFU2012-35367, IDC-20101173 and 2009SGR-1036)Peer Reviewe

Spike avalanches in vivo suggest a driven, slightly subcritical brain state

In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1005B, Bernstein Zentrum für Computational Neuroscience, Göttingen - Kooperative Dynamiken und Adaptivität in neuronalen SystemenBMBF, 01GQ0742, Verbundprojekt Bernstein Partner: Gedächtnis-Netzwerk, Teilprojekt

Neo: an object model for handling electrophysiology data in multiple formats

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.EC/FP7/269921/EU/Brain-inspired multiscale computation in neuromorphic hybrid systems/BrainScaleSDFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1302, Nationaler Neuroinformatik Knote

Neue Werkzeuge zur Analyse elektrophysiologischer Daten und ihre Anwendung auf eine Arbeitsgedächtnis-Studie

Um zu verstehen, wie neuronale Signale mit Verhalten zusammenhängen, ist es essentiell die Aktivität von einzelnen Neuronen zu untersuchen. Elektrophysiologie ist eine der ältesten und wichtigsten Techniken um die Spannungsfluktuationen aufzunehmen, die von aktiven Neuronen produziert werden. In extrazellulärer Elektrophysiologie werden Elektroden im interzellulären Medium platziert. Diese Elektroden nehmen oft mehrere Neuronen gleichzeitig auf. Ein zentrales Problem ist es, herauszufinden welche aufgenommenen Spikes von welchen Neuronen produziert wurden. Lösungen für dieses Problem sind bekannt als Spike Sorting. Die kontinuierlichen Verbesserungen von elektrophysiologischen Messtechniken machen Verbesserungen in der Datenanalyse notwendig: Die wachsenden Mengen an aufgenommen Daten müssen verwaltet werden und passende Analysealgorithmen für große Datensätze müssen entwickelt werden. Weil die Experimente komplexer werden, wird interdisziplinäre Kooperation wichtiger, was zu einem gesteigerten Interesse an neuen Methoden zur Datenfreigabe und kollaborativer Analyseentwicklung führt. Der erste Teil dieser Arbeit widmet sich einer Lösung für die Herausforderungen, die dieser Trend aufwirft: Spyke Viewer ist eine Softwareplattform zum Verwalten und Analysieren von elektrophysiologischen Daten. Der Fokus von Spyke Viewer liegt auf Nutzerfreundlichkeit und Flexibilität, was das Programm für Experimentatoren zum Visualisieren von Daten und für Theoretiker zum Entwickeln von Algorithmen nützlich macht. Bei neueren elektrophysiologischen Techniken, die gleichzeitig die Aktivität von vielen Neuronen aufnehmen, überlappen aufgenommene Spikes häufig zeitlich. Die resultierenden Wellenformen sind eine besondere Herausforderung für Spike Sorting Methoden. In dieser Arbeit wird ein Spike Sorting Algorithmus verbessert und evaluiert, der das Problem von zeitlich überlappenden Wellenformen löst. Alle Methoden wurden auf einem empirischen Datensatz entwickelt und getestet, der im präfrontalen Cortex von Makaken aufgenommen wurde, während die Affen eine visuelle Gedächtnisaufgabe lösen mussten. Mit Spyke Viewer und dem verbesserten Spike Sorting Algorithmus wurden großflächige Analysen zum Code der visuellen Stimuli und Experimentalkonditionen durchgeführt. Sowohl Reaktionen von einzelnen Neuronen als auch Populationscodes wurden mit einer Vielfalt von Dekodierungsmethoden untersucht. Mit Hilfe von zeitaufgelösten Analysen wird gezeigt, dass sich die Codierung von allen Experimentalkonditionen schnell ändert, während die Codierung der Stimuli zwischen Versuchs- und Test-Stimuli weitgehend gleich bleibt.In order to understand how signals in the brain are related to observed behavior, it is essential to observe the activity of single neurons. Electrophysiology is one of the oldest and most important means to achieve recordings of the voltage fluctuations produced by active neurons. In extracellular electrophysiology, recording electrodes are placed in the intercellular medium and often record from multiple neurons at the same time. A central issue is to determine which neuron produced each recorded spike, solutions for this problem are called spike sorting. The steady improvement in electrophysiological recording techniques requires advances in data analysis: the increasing amounts of recorded data need to be managed and appropriate analysis algorithms for large data sets need to be developed. As experiments grow more complex, interdisciplinary cooperation becomes more important. This necessitates data sharing and collaborative development of analyses. The first part of this thesis is concerned with a solution to the challenges arising from this trend: Spyke Viewer is a software platform for electrophysiological data management and analysis that supports many different data formats in a unified way. It is focused on usability and flexibility, so it is useful to both experimenters who can use it to browse and visualize data and theoreticians who develop new analysis algorithms. With newer electrophysiology techniques, where multiple recording channels capture activity from an increasing amount of neurons, recorded spikes often overlap in time. The resulting waveforms are a particular challenge for spike sorting methods. In this thesis, a spike sorting algorithm that addresses the overlap problem is improved and evaluated on simulated and empirical data. In addition, a complete spike sorting pipeline from raw data to sorted spikes is described. All methods were developed and tested using an empirical data set recorded from the prefrontal cortex of macaque monkeys. The monkeys performed a visual working memory task. Using Spyke Viewer and the improved spike sorting algorithm, large scale analyses on the coding of visual stimuli and experimental conditions were carried out. Reactions of individual neurons were examined and population codes were explored using a variety of decoding methods. Using time-resolved analyses, it was found that the neural coding of all experimental conditions changes quickly over the course of a trial, but sample stimuli and test stimuli elicit very similar neural response patterns at different times during the trial

epiasini/pymuvr: 1.3.1

This release is identical to 1.3.0; its only purpose is to add Zenodo integration to the GitHub repo