Search

We present a web-based neuron morphology viewer that accepts a variety of neuron morphology file formats (Neurolucida DAT/NRX/ASC/XML, SWC) and presents the resulting neuronal tree as an interactive webpage that consists of both a data-hierarchy and a 3D graphical rendering of the neuron. The viewer was created to reconcile commonly used morphology file formats into a single internal representation.

Tract-tracing experiments are considered the gold standard when it comes to revealing structural connectivity in the brain. They provide a direct measurement of the axonal tracts of the neuron(s) with axons that start or terminate close to the injection site. Rapid developments in tracing techniques have resulted in a large body of literature, in which each publication describes a few experiments with a given protocol, species and brain atlas. For mouse, several large scale studies are under way in which brain-wide connectivity is measured with an automated tracing and analysis workflow. For ethical reasons however, such initiatives are unlikely to be applied to large mammals. The alternative is to rely on databases which combine findings from individual publications. We focus on a prime example of such a database: the CoCoMac collation of Macaque connectivity. Can the very diverse set of experimental protocols and nomenclatures be combined into a reliable wiring diagram of the brain? To study this, we built a new online platform for CoCoMac (cocomac.g-node.org) [1] with two essential components: (1) a custom wiring diagram builder, in which every detail of the included tracing experiment can be specified; and (2) a graphical nomenclature consistency tool, in which conflicting naming schemes can be resolved interactively. In this talk I will show that small errors in relating brain regions across atlases can have a detrimental effect on the resulting wiring diagram. With the consistent nomenclature, we continue to show different views of the connectivity matrix, such as retrograde vs. anterograde, intralateral vs. contralateral and with and without certain types of tracer. Finally we study the variability of the matrix by leaving out random subsets of tracing data. This variability predicts the effect of adding new data to CoCoMac, and indicates which brain regions require special attention.

The wonderful application that Google has built to make virtual tours on the planet earth has inspired many to propose a similar application for the brain. Quite a few applications have been built with Google earth in mind, and in the funding application. Very useful products came out, but none of these came close to the desired user experience. This lecture will provide a journey through the systems that have been built, and evaluate the outcome. What makes the brain so much more challenging than planet earth? Which strategies to simplify the brain representation have been successful? Will the Human Brain Project (start October 2013) help to resolve the remaining issues?

We present a web-based framework to (1) set up a relational data base; (2) search and browse its contents interactively; and (3) edit/add content to it. Central to the framework is a database definition file: a text file that describes the database structure in a compact (JSON-based) format. This file can either be written from scratch to set up a new database, or be extracted from an existing database. Multiple versions of the definition file can provide different views (intranet/world wide web) of the same database. The platform provides an interactive search/browse wizard that (1) allows the user to compose complex queries without needing to know the database structure; and (2) presents the search results in tabular form, with interactive (AJAX-based) links to related child and parent tables.

Microelectrode Array (MEA) measurements from in vitro slices has become an important research tool in neuroscience, however the interpretation of such recordings is not always straightforward. We have developed a modeling framework for emulating in vitro MEA recordings that takes into account both the measurement physics of the MEA set-up, and the underlying neural activity of the slice, resulting in simulated data that closely resembles experimental recordings. Our modeling framework may aid interpretation of experimental data by reproducing the experimental procedure in silico, make experimentally testable predictions, and produce test-data for validating various analysis methods such as CSD estimates and spike-sorting algorithms. Our simulations are separated into two domains; the first step is simulations of neuronal activity in populations of multi-compartment model neurons, and secondly solving the electrostatic forward problem in the extracellular space. For the neuronal simulations we employ LFPy [1], a Python module built upon NEURON's Python interface [2] to obtain the transmembrane currents in every compartment of the model neurons. Then the Finite Element Method (FEM) is used to solve the Poisson equation from electrostatics and calculate the extracellular potentials in the 3D volume including the electrode sites, and test various approximation schemes. Hence, the effects of the electrodes can be assessed together with the impact of inhomogeneities and anisotropies of the extracellular medium in recordings. The approach is in principle applicable to any multicompartment neuron model (from e.g. ModelDB [3]), any neuron number or any MEA electrode set-up. We will present our modeling framework, together with an investigation of the electrode effects on the measured signals. Then we will go on to present two different applications. Firstly, we have produced spike-sorting test-data to benchmark automated spike-sorting algorithms [4] used on MEA recordings. This project is part of an international coordinated effort where such test-data will be collected and made available at http://spike.g-node.org, allowing exchange of synthetic and experimental test-data with known underlying activity, and systematic benchmarking and comparison of spike-sorting algorithms applied to such data [5]. Secondly we will present a project where we have been studying the LFP signature of single neurons receiving varying, sub-threshold sinusoidal current input measured by MEAs in an acute brain slice setting [6]. The model output is compared to corresponding experimental data, which includes the detailed reconstruction of the excited neuron.