1 - 50 of 129 results

PrAGMATiC / Probabilistic And Generative Model of Areas Tiling the Cortex

Provides a detailed semantic atlas. PrAGMATiC determines where functional areas appear on the cortical sheet and determines how the cortical map is produced from an arrangement of areas. It can be modified to model functional gradients explicitly. The tool can be used for determining whether the semantic maps found here are best described as homogeneous areas or as gradients. It is based on a Voronoi diagram that must assign an area to every point on the cortex.

NeuroGPS-Tree / NeuronGlobalPositionSystem-Tree

Reconstructs neuronal population from image stacks. NeuroGPS-Tree is built on NeuroGPS software. In NeuroGPS-Tree reconstruction, individual neuronal trees can be identified and quantified. NeuroGPS-Tree reconstructs neuronal populations by partially mimicking the strategy used by experienced annotators and progressively approaches an accurate reconstruction by repetitively using statistical information about neuronal morphology at multiple scales. These features make NeuroGPS-Tree an effective tool for analyzing data sets in which the neurite density is too complex for previously established methods. NeuroGPS-Tree is also suitable for the analysis of large-scale data sets, and it may be useful for mapping neuronal circuits.


Enables largely automated processing of magnetic resonance images (MRI) of the human brain. BrainSuite provides a sequence of low-level operations that can produce accurate brain segmentations in clinical time. It produces classified brain volumes that can be useful for quantitative studies of different regions of the brain. The tool consists of several modules that performs skull and scalp removal, nonuniformity correction, tissue classification, and object topology correction.

The Virtual Brain

Simulates the dynamics of large-scale brain networks with biologically realistic connectivity. The Virtual Brain uses tractographic data (DTI/DSI) to generate connectivity matrices and build cortical and subcortical brain networks. The connectivity matrix defines the connection strengths and time delays via signal transmission between all network nodes. Various neural mass models are available in the repertoire of The Virtual Brain and define the dynamics of a network node. Together, the neural mass models at the network nodes and the connectivity matrix define the Virtual Brain. The Virtual Brain simulates and generates the time courses of various forms of neural activity including Local Field Potentials (LFP) and firing rate, as well as brain imaging data such as EEG, MEG and BOLD activations as observed in fMRI.

CBS Tools

Allows ultra-high-resolution brain segmentation at 7 Tesla (T). CBS Tools is an automated computational framework for brain segmentation and cortical reconstruction at the ultra-high resolution of 0.4 mm, based on quantitative T1 images acquired at 7 T with the MP2RAGE sequence. The software is implemented as a plug-in for the MIPAV and JIST medical image processing platforms. It supports many image formats, and includes a user-friendly interface for image visualization and editing as well as a graphical pipeline engine for large-scale processing.

Dipy / Diffusion Imaging in Python

Allows to study diffusion Magnetic Resonance Imaging (MRI) data. Dipy is a program allowing users to share their code and experiments. One of its objectives is to provide transparent implementations for all the different steps of the dMRI analysis with a uniform programming interface. It implements two interfaces for probabilistic Markov fiber tracking: (1) it allows the user to provide the distribution evaluated on a discrete set of possible tracking directions, and (2) it accommodates tracking methods where the fiber orientation distribution function (fODF) cannot be easily computed.


Provides a simulation environment for modeling individual neurons and networks of neurons. NEURON provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON is designed to let the users deal directly with familiar neuroscience concepts. Consequently, they can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells.


Helps researchers understand brain function at the level of the topographic maps that make up sensory and motor systems. Topographica is a software package for computational modeling of neural maps. It is intended to complement the many good low-level neuron simulators that are available, such as Genesis and Neuron. This method focuses on the large-scale structure and function that is visible only when many thousands of such neurons are connected into topographic maps containing millions of connections.


Facilitates the creation, visualization, and analysis of networks of multicompartmental neurons in 3D space. neuroConstruct provides a graphical user interface (GUI) which allows model generation and modification without programming. Models within neuroConstruct are based on new simulator-independent NeuroML standards, allowing automatic generation of code for NEURON or GENESIS simulators. neuroConstruct was tested by reproducing published models and its simulator independence verified by comparing the same model on two simulators. neuroConstruct can be used for teaching network function in health and disease. The 3D models generated will allow simulations of increased biological realism, enabling more direct comparisons with results from new experimental methods for measuring neural activity in 3D at high spatial and temporal resolution.

PRoNTo / Pattern Recognition for Neuroimaging Toolbox

Provides a method for multivariate analysis based on machine learning models for neuroimaging data. PRoNTo is open-source, cross-platform, MATLAB-based and Statistical Parametric Mapping (SPM) compatible, therefore being suitable for both cognitive and clinical neuroscience research. It can also be extended via the addition of new feature selection and extraction approaches, validation procedures or classification/regression models.


Aims to the collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules’ local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly’s entire brain by integration of their independently developed models of its constituent processing units.

BRAPH / BRain Analysis using graPH theory

Allows connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized.

GC-LDA / Generalized Correspondence Latent Dirichlet Allocation

Generates topics that are simultaneously constrained by both anatomical and functional considerations. GC-LDA learns latent topics from the meta-analytic Neurosynth database of over 11,000 published functional magnetic resonance imaging (fMRI) studies. It allows researchers to formally specify priors on the GC-LDA topics, providing a powerful means of contextualizing interpretations and accounting for prior expectations and beliefs.

GRETNA / GRaph thEoreTical Network Analysis

Performs comprehensive graph-based topological analyses of brain networks. GRETNA incorporates network construction, analysis and comparison modules to provide a complete and automatic pipeline for connectomics. It allows manipulation of different network analytical strategies, including structurally, functionally or randomly defined network nodes, positive or negative connectivity processing, binary or weighted network types and the choices of different thresholding procedures or ranges.

MENGA / Multimodal Environment for Neuroimaging and Genomic Analysis

Allows exploration of correlation patterns between neuroimaging data with Allen human brain database (ABA) mRNA gene expression profiles. MENGA was applied to six different imaging datasets that target the dopamine and serotonin receptor systems and the myelin molecular structure in the human brain. It is useful to compare genomic and imaging data. This tool gives a quantitative assessment of the amount of the variability in the image phenotype.

FFBO / Fruit Fly Brain Observatory

Aims to study fruit fly brain function and investigate fruit fly brain disease models that are highly relevant to the mechanisms of human neurological and psychiatric disorders. FFBO stores and processes data related to the neural circuits of the fly brain including location, morphology, connectivity and biophysical properties of every neuron. It seamlessly integrates the structural and genetic data from multiple sources that can be queried, visualized and interpreted. Furthermore, it automatically generates models of the fly brain that can be simulated efficiently using multiple Graphics Processing Units (GPUs) to help elucidate the mechanisms of human neurological disorders and identify drug targets.

GeNN / GPU enhanced Neuronal Network

A framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA graphics processing units, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ.


A deep neural network model architecture that is highly optimized for serial-section transmitted electron microscopy image segmentation. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. While the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics.

ECoG ClusterFlow

Allows neuroscientists to investigate the major cluster evolution patterns over space and time. ECoG ClusterFlow is a hierarchical multi-scale approach that supports the exploration, comparison and analysis of time-varying community evolution patterns at varying temporal granularity. It provides (i) an overview that summarizes the overall changes in cluster evolution, where users explore salient dynamic patterns; and (ii) a hierarchical glyph-based timeline visualization for exploring the dynamic spatial organizational changes of the clusters that uses data aggregation and small multiples methods.


Provides a simulator for spiking neural networks. Brian can be used to create a network of neurons and synapses connected with a network of astrocytes. This method permits user to focus mainly on the details of their models than on their implementation. Neuron models are defined by writing differential equations in standard mathematical notation, or facilitating scientific communication. This tool is useful for teaching computational neuroscience and for neuroscientific modelling at the systems level.


Provides a Graphic Processing Unit (GPU)-accelerated library for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail. CARLsim allows execution of networks of Izhikevich spiking neurons with realistic synaptic dynamics on both generic x86 CPUs and standard off-the-shelf GPUs. The simulator provides a PyNN-like programming interface in C/C++, which allows for details and parameters to be specified at the synapse, neuron, and network level.


Provides a library allowing users to handle large-scale artificial neural networks. TensorFlow is an open source software, compatible with various languages such as Python or C++, permitting to train and test neural networks by building computational graphs. The application is able to perform classification tasks and aims to assign a network into a predefined class thanks to iterative updates of weights and biases associated with each input. Besides, results can be visualized by using an additional visualization toolkit.


Furnishes a method for tracking real-time auditory attention from non-invasive M/EEG recordings. Real-time-Tracking-of-Selective-Auditory-Attention is a software, based on Bayesian filtering, that performs in three steps: (i) estimation of dynamic models of encoding and decoding in real-time; (ii) extracting an attention-modulated feature; and (iii) determination of the given feature by using a state-space simulator and translation of the results to provide an evaluation of the attentional state.


Allows researchers to measure the quality of their dynamic functional connectivity (DFC) methods. dfcbenchmarker is based on four simulation methods: (1) the sliding window (SW) method that uses a continuous subsection of the data; (2) the tapered sliding window (TSW) method that is similar to the SW adding a larger weight to the time points closer to the centre of the window; (3) the spatial distance (SD) that uses data points that have similar spatial profiles and (4) the Jackknife Correlation (JC) method.