Neuroinformatics covers the informatics side of modern neuroscience and includes the development of analysis tools, databasing, and computational modeling.

Within the CNCR a growing number of groups applies neuroinformatics techniques for advanced data analysis and integration of experiment and theory. 

Databasing
Databases are used to store and manage large datasets generated in the CNCR, ranging from genetic, morphological, physiological and behavioral data. We are partner in two European initiatives (www.synsys.eu and www.eurospin.mpg.de) to integrate this data in central databases in Europe. In addition we have access to large databases with genomic data from patient cohorts world wide for genome wide association studies (GWAS).

Analysis tools
Several analysis tools are developed in-house for fast and accurate data analysis of large datasets. We developed software for the automated analysis of vesicle distribution in electron microscopy (EM) images of neurons and secretory cells, morphology analysis of neurons and synapses in confocal images, vesicle tracking in live imaging data, analysis of electrophysiology data, and auto-correlation in EEG and MRI data.

Computing facilities
For computationally demanding tasks we have access to a large cluster computing facility www.geneticcluster.org coordinated by Dr. Posthuma. 

Computational modeling
Within the CNCR computational modeling is applied to a broad spectrum of projects, including single compartment models for synaptic processes, interaction networks for synaptic proteins, and multi-compartmental models for neurons and networks. In some cases probabilistic methods like Bayesian models or mixture of probabilistic PCA’s are used to account for sparseness of parameter information and experimental variation in datasets. In all cases the aim is to integrate modeling and experimental research by using models to simulate experimental observations and predict new results. Models are implemented in C++, Python, Matlab or the simulation environment NEURON.