What is NeuroSuites?

NeuroSuites is an online platform to run multiple neuroscience tools in a very easy way.

To start using NeuroSuites just click on your preferred category on the tab at the top of the page.

It provides you multiple tools to analyze neuroscience data. You will not need to install anything to run the tools provided here, everything is online. Furthermore, when you are done, you can export your results to your own computer.

Who can use NeuroSuites?

Everyone can use NeuroSuites but it is intended to neuroscientists or data scientists that need to use neuroscience software in a fast and easy way without installing a lot of software in their computers.

No computer science or programming knowledge are required.

Machine learning

We have many available tools , some are focused in analyzing neurons morphology reconstructions and the others are general purpose tools like the statistics engine, supervised classification models, Bayesian networks, etc.

You can upload your own data set or you can select the neurons in the NeuroMorpho.org database.

Then you can use the following tools to analyze your neurons:

L-Measure - Extract morphological measurements

This tool allows researchers to extract quantitative morphological measurements from neuronal reconstructions.

NeuroViewer - 3D Neuron reconstruction

3D Neuron reconstruction visualization package

Statistics engine

Descriptive statistics:univariate, bivariate and multivariate analysis and visualization.

Inferential statistics: confidence intervals, hypothesis testing (one sample t-test, two independent samples t-test).

Interactive plots with Plotly (histograms, probability density functions, box plots, 2D and 3D scatter plots, Chernoff faces, Radar charts, Parallel coordinates, Andrew curves and much more!), custom options, exporting formats, etc. Everything online. Check out the L-Measure tool in the Morphometric Analyzer to see it in action.

Machine learning models

Bayesian Networks: structure and parameters learning for continuous and discrete datasets. Full visualization and inference for continuous BNs.

Probabilistic clustering graphical models: full visualization and inference for continuous models.

Supervised classification: learning and inference of multiple supervised classification models: k-Nearest neighbors, rule induction (CN2), decision tree, random forest, SVM, neural network, LDA, QDA, logistic regression, Naive Bayes, TAN, bagging meta-classifier, boosting (AdaBoost) meta-classifier, stacking meta-classifier. It also lets to perform classification for a multi-label classification problem using problem transformation approaches like binary relevance, classifier chains, label powerset, or RAKELd. The other approach for this kind of problems is with algorithm adaptation methods like multi-label knn or multi-label SVM.

Non probabilistic clustering: Discover groups in the data using multiple non probabilisitc clustering methods models: hierarchical agglomerative clustering, k-means, dbscan, affinity propagation and spectral. after it you can download the data with clusters and visialize them on a PCA with 2 components plot.

NeuroSTR - Validator, format converter

NeuroSTR is a neuroanatomy toolbox. It reads and processes three-dimensional neuron reconstructions in the most common file formats and offers a huge set of functions and utilities to work with them.

3DBasalRM - Repair cut-points in the basal arborization

Data-driven repairing model that detects cut-points in the basal arborization and then repairs them using a growth model built from complete three-dimensional neuron reconstructions As result a neuron in JSON format is returned.

GabaClassifier - Interneuron classifier

Classifies the given interneuron morphology into one of the 8 possible classes.

3DspineS - Dendritic spine simulation

This mathematical approach could provide a useful tool for theoretical predictions on the functional
features of human pyramidal neurons based on the morphology of dendritic spines.

3DSomaMS - Delimit the neuronal soma

This software provides a mathematical definition and an automatic segmentation method to delimit theneuronal soma.

3DSynapsesSA - Analyze spatial distribution of cortical synapses

Process and analyze patterns in the three-dimensional spatialdistribution of cortical synapses.

Dendrite arborization simulation

Generation of synthetic neurons with soma and dendrites.