Activity-Dependent Calcium Signaling in Neurons of the Medial Superior Olive during Late Postnatal Development.

The development of sensory circuits is partially guided by sensory experience. In the medial superior olive (MSO), these refinements generate precise coincidence detection to localize sounds in the azimuthal plane. Glycinergic inhibitory inputs to the MSO, which tune the sensitivity to interaural time differences, undergo substantial structural and functional refinements after hearing onset. Whether excitation and calcium signaling in the MSO are similarly affected by the onset of acoustic experience is unresolved. To assess the time window and mechanism of excitatory and calcium-dependent refinements during late postnatal development, we quantified EPSCs and calcium entry in MSO neurons of Mongolian gerbils of either sex raised in a normal and in an activity altered, omnidirectional white noise environment. Global dendritic calcium transients elicited by action potentials disappeared rapidly after hearing onset. Local synaptic calcium transients decreased, leaving a GluR2 lacking AMPAR-mediated influx as the only activity-dependent source in adulthood. Exposure to omnidirectional white noise accelerated the decrease in calcium entry, leaving membrane properties unaffected. Thus, sound-driven activity accelerates the excitatory refinement and shortens the period of activity-dependent calcium signaling around hearing onset. Together with earlier reports, our findings highlight that excitation, inhibition, and biophysical properties are differentially sensitive to distinct features of sensory experience.SIGNIFICANCE STATEMENT Neurons in the medial superior olive, an ultra-fast coincidence detector for sound source localization, acquire their specialized function through refinements during late postnatal development. The refinement of inhibitory inputs that convey sensitivity to relevant interaural time differences is instructed by the experience of sound localization cues. Which cues instruct the refinement of excitatory inputs, calcium signaling, and biophysical properties is unknown. Here we demonstrate a time window for activity- and calcium-dependent refinements limited to shortly after hearing onset. Exposure to omnidirectional white noise, which suppresses sound localization cues but increases overall activity, accelerates the refinement of calcium signaling and excitatory inputs without affecting biophysical membrane properties. Thus, the refinement of excitation, inhibition, and intrinsic properties is instructed by distinct cues.

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.

A distributed code for color in natural scenes derived from center-surround filtered cone signals.

In the retina of trichromatic primates, chromatic information is encoded in an opponent fashion and transmitted to the lateral geniculate nucleus (LGN) and visual cortex via parallel pathways. Chromatic selectivities of neurons in the LGN form two separate clusters, corresponding to two classes of cone opponency. In the visual cortex, however, the chromatic selectivities are more distributed, which is in accordance with a population code for color. Previous studies of cone signals in natural scenes typically found opponent codes with chromatic selectivities corresponding to two directions in color space. Here we investigated how the non-linear spatio-chromatic filtering in the retina influences the encoding of color signals. Cone signals were derived from hyper-spectral images of natural scenes and preprocessed by center-surround filtering and rectification, resulting in parallel ON and OFF channels. Independent Component Analysis (ICA) on these signals yielded a highly sparse code with basis functions that showed spatio-chromatic selectivities. In contrast to previous analyses of linear transformations of cone signals, chromatic selectivities were not restricted to two main chromatic axes, but were more continuously distributed in color space, similar to the population code of color in the early visual cortex. Our results indicate that spatio-chromatic processing in the retina leads to a more distributed and more efficient code for natural scenes.