Philipp L. Rautenberg

TL;DR: This work proposes here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations, and develops an open source implementation in the Python programming language that should become the standard basis for Python tools in neurophysiology.

TL;DR: The developmental profile of the morphology of MSO neurons obtained indicates that maturation is reached 2 weeks after hearing onset, and this work found that developmental refinement occurs until P27, generating morphologically compact, cylinder‐like cells with axons originating from the soma.

TL;DR: 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, can be automated to large degree using the G-Node Python Library.

TL;DR: Experimental validations show the superiority of the proposed technique over the state-of-the art methods, in terms of precision-recall trade off for a particular code size, which demonstrates the potential of this approach for effective morphology preserving encoding and retrieval in large neuron databases.

TL;DR: GNData is described, a data management platform for neurophysiological data that 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).