Displaying 1 to 13 from 13 results

awesome-neuroscience - A curated list of awesome neuroscience libraries, software and any content related to the domain

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Curated list of awesome neuroscience libraries, software and any content related to the domain. Neuroscience is the study of how the nervous system develops, its structure, and what it does. Neuroscientists focus on the brain and its impact on behavior and cognitive functions. Traditionally, neuroscience has been seen as a branch of biology, but it has grown to encompass a wide range of interdisciplinary fields that work together toward elucidating brain function at multiple levels of investigation.

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  •    Go

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Zyan Drench, a game for Android

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Zyan Drench is a simple yet very entertaining game for Android phones developed using Zyan Communication Framework: http://zyan.com.de




hubot-mongodb-brain - MongoDB brain for Hubot

  •    CoffeeScript

MongoDB brain for Hubot. Support MongoLab and MongoHQ on Heroku. Hubot's default redis-brain saves all data into one large blob (It's not using Redis as KVS) and write it every 20 seconds. So it exceeds maxmemory of Redis.

BRAINSTools - A suite of tools for medical image processing focused on brain analysis

  •    C++

The BRAINSTools is a harness to assist in building the many of the BRAINSTools under development. Developers should run the ./Utilities/SetupForDevelopment.sh script to get started.

brain-monitor - A terminal app written in Node.js to monitor brain signals in real-time

  •    Javascript

brain-monitor is a command line dashboard that displays your raw brain signals in real time. Make sure your EPOC headset is connected, and your terminal is not too tiny.

brainjs - Flexible library for creating, training and analyzing artificial neural networks

  •    Javascript

Flexible library for creating, training and analyzing multi-layer feed-forward artificial neural networks. Networks consist of an array describing the number and size of its layers, a matrix of weights and an activation function used in the neurons.


Python_OpenBCI

  •    Python

Running the Python server/client without the --json flag will cause the OpenBCISample object to be used as the data transmission mechanism. This is for people that want to do some processing in Python.

NEURON-UI - NEURON User Interface

  •    AMPL

This repository hosts an experimental prototype for a new user interface for NEURON based on web technologies. First install Docker from here.

3D-GAN-superresolution - 3D super-resolution using Generative Adversarial Networks

  •    Python

Here we present the implementation in TensorFlow of our work to generate high resolution MRI scans from low resolution images using Generative Adversarial Networks (GANs), accepted in the Medical Imaging with Deep Learning Conference – Amsterdam. 4 - 6th July 2018. In this work we propose an architecture for MRI super-resolution that completely exploits the available volumetric information contained in MRI scans, using 3D convolutions to process the volumes and taking advantage of an adversarial framework, improving the realism of the generated volumes. The model is based on the SRGAN network. The adversarial loss uses least squares to stabilize the training and the generator loss, in addition to the adversarial term contains a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore three different methods for the upsampling phase: an upsampling layer which uses nearest neighbors to replicate consecutive pixels followed by a convolutional layer to improve the approximation, sub-pixel convolution layers as proposed in Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network and a modification of this method Checkerboard artifact free sub-pixel convolution that alleviates checkbock artifacts produced by sub-pixel convolution layers (Check: Deconvolution and Checkerboard Artifacts for more information).

life - Please use the new version of LiFE: www.github.com/brain-life/encode

  •    Matlab

Statistical evaluation for brain connections and tracts. Standard tractography can use diffusion measurements from a living brain to generate a large collection of candidate white-matter fascicles; the connectome. Linear Fascicle Evaluation (LiFE) takes any connectome and uses a forward modelling approach to predict diffusion measurements in the same brain. LiFE predicts the measured diffusion signal using the orientation of the fascicles present in a connectome. LiFE uses the difference between the measured and predicted diffusion signals to measure prediction error. The connectome model prediction error is used to compute two metrics to evaluate the evidence supporting properties of the connectome. One metric -the strength of evidence - compares the mean prediction error between alternative hypotheses. The second metric - the earth movers distance - compares full distributions of prediction error. These metrics can be used for: 1. Comparing tractography algorithms 2. Evaluating the quality of tractography solutions for individual brains or group of brains and 3. Testing hypotheses about white-matter tracts and connections.

neuron - Neural networks toolbox for anatomical image analysis

  •    Python

A set of tools and infrastructure for medical image analysis with neural networks. While the tools are somewhat general, neuron will generally run with keras on top of tensorflow. Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation AV Dalca, J Guttag, MR Sabuncu IEEE CVPR: Conference on Computer Vision and Pattern Recognition. 2018.