# An overview of open source Deep Learning-based libraries for Neuroscience

arXiv: https://arxiv.org/abs/2301.05057 · HTML: https://mlsanity.com/paper/2301.05057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 92 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset](https://mlsanity.com/paper/2206.06694/) (2022)
- [NiftyNet: a deep-learning platform for medical imaging](https://mlsanity.com/paper/1709.03485/) (2017)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics](https://mlsanity.com/paper/2106.02749/) (2021)
- [DeepNeuro: an open-source deep learning toolbox for neuroimaging](https://mlsanity.com/paper/1808.04589/) (2018)
- [ivadomed: A Medical Imaging Deep Learning Toolbox](https://mlsanity.com/paper/2010.09984/) (2020)
- [AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks](https://mlsanity.com/paper/1711.01004/) (2017)
- [A Systematic Review of Natural Language Processing for Knowledge Management in Healthcare](https://mlsanity.com/paper/2007.09134/) (2020)
- [ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation](https://mlsanity.com/paper/2201.04065/) (2022)

## Built on by (most cited first, 1 of 1)

- [SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography](https://mlsanity.com/paper/2401.05405/) (2023)

## Abstract (arXiv)

In recent years, deep learning revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis. Due to the fast growth of the domain, it could be a complicated and extremely time-consuming task for worldwide researchers to have a clear perspective of the most recent and advanced software libraries. This work contributes to clarify the current situation in the domain, outlining the most useful libraries that implement and facilitate deep learning application to neuroscience, allowing scientists to identify the most suitable options for their research or clinical projects. This paper summarizes the main developments in Deep Learning and their relevance to Neuroscience; it then reviews neuroinformatic toolboxes and libraries, collected from the literature and from specific hubs of software projects oriented to neuroscience research. The selected tools are presented in tables detailing key features grouped by domain of application (e.g. data type, neuroscience area, task), model engineering (e.g. programming language, model customization) and technological aspect (e.g. interface, code source). The results show that, among a high number of available software tools, several libraries are standing out in terms of functionalities for neuroscience applications. The aggregation and discussion of this information can help the neuroscience community to devolop their research projects more efficiently and quickly, both by means of readily available tools, and by knowing which modules may be improved, connected or added.