Neuron-Miner: An Advanced Tool for Morphological Search and Retrieval in Neuroscientific Image Databases.

The steadily growing amounts of digital neuroscientific data demands for a reliable, systematic, and computationally effective retrieval algorithm. In this paper, we present Neuron-Miner, which is a tool for fast and accurate reference-based retrieval within neuron image databases. The proposed algorithm is established upon hashing (search and retrieval) technique by employing multiple unsupervised random trees, collectively called as Hashing Forests (HF). The HF are trained to parse the neuromorphological space hierarchically and preserve the inherent neuron neighborhoods while encoding with compact binary codewords. We further introduce the inverse-coding formulation within HF to effectively mitigate pairwise neuron similarity comparisons, thus allowing scalability to massive databases with little additional time overhead. The proposed hashing tool has superior approximation of the true neuromorphological neighborhood with better retrieval and ranking performance in comparison to existing generalized hashing methods. This is exhaustively validated by quantifying the results over 31266 neuron reconstructions from Neuromorpho.org dataset curated from 147 different archives. We envisage that finding and ranking similar neurons through reference-based querying via Neuron Miner would assist neuroscientists in objectively understanding the relationship between neuronal structure and function for applications in comparative anatomy or diagnosis.

NeuronDepot: keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications.

Neuroscience today deals with a "data deluge" derived from the availability of high-throughput sensors of brain structure and brain activity, and increased computational resources for detailed simulations with complex output. We report here (1) a novel approach to data sharing between collaborating scientists that brings together file system tools and cloud technologies, (2) a service implementing this approach, called NeuronDepot, and (3) an example application of the service to a complex use case in the neurosciences. The main drivers for our approach are to facilitate collaborations with a transparent, automated data flow that shields scientists from having to learn new tools or data structuring paradigms. Using NeuronDepot is simple: one-time data assignment from the originator and cloud based syncing-thus making experimental and modeling data available across the collaboration with minimum overhead. Since data sharing is cloud based, our approach opens up the possibility of using new software developments and hardware scalabitliy which are associated with elastic cloud computing. We provide an implementation that relies on existing synchronization services and is usable from all devices via a reactive web interface. We are motivating our solution by solving the practical problems of the GinJang project, a collaboration of three universities across eight time zones with a complex workflow encompassing data from electrophysiological recordings, imaging, morphological reconstructions, and simulations.

Integrated platform and API for electrophysiological data.

Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines.

Data management routines for reproducible research using the G-Node Python Client library.

Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow.

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.

Quantification of the three-dimensional morphology of coincidence detector neurons in the medial superior olive of gerbils during late postnatal development.

In the mammalian medial superior olive (MSO), neurons compute the azimuthal location of sound sources by temporally precise coincidence detection. It is assumed that the dendritic morphology of MSO neurons plays a crucial role in this computational process. However, few quantitative data about the morphology of these neuronal coincidence detectors are available, limiting theoretical approaches. Such a quantitative morphological description of neurons of the mammalian MSO would also allow a comparative analysis with its avian analog, the nucleus laminaris. We used single-cell electroporation, microscopic reconstruction, and compartmentalization to extract anatomical parameters of MSO neurons and quantitatively describe their morphology and development between postnatal day 9 and 36. We found that developmental refinement occurs until P27, generating morphologically compact, cylinder-like cells with axons originating from the soma. The complexity of higher order dendrites decreases between postnatal days 9 and 21. This decrease in dendritic complexity is judged from counting and analyzing the location of dendritic branches and determining the distribution of the surface area and total length of neurons. During this developmental period, the average length of terminal branches increases about twofold, indicating an elimination of predominantly small branches. The cell volume increases more than 1.5-fold between P9 and P27, a change that can be attributed to an increase in dendritic diameter during this developmental period. The developmental profile of the morphology of MSO neurons obtained indicates that maturation is reached 2 weeks after hearing onset.