# An Overview of Open Source Deep Learning-Based Libraries for Neuroscience
## 2 Background
### Neuroinformatics  
Neuroscience is evolving into a data-centric discipline. Modern research heavily depends on human researchers as well as machine agents to store, manage, and process computerized data from the experimental apparatus to the end stage. Before delving into the specifics of artificial neural networks applied to the study of biological neural systems, it is useful to outline the broader concepts of neuroinformatics, regarding data and coding, especially in the light of open culture. According to the International Neuroinformatics Coordinating Facility (INCF), <<neuroinformatics is a research field devoted to the development of neuroscience data and knowledge bases together with computational models and analytical tools for sharing, integration, and analysis of experimental data and advancement of theories about the nervous system function.>> [21]. Given the relevance of neuroinformatics to neuroscience, supporting open and reproducible science implies and requires attention to standards and best practices regarding open data and code. The INCF itself is an independent organization devoted to validating and promoting such standards and practices, interacting with the research communities [22] and aiming at the \"FAIR principles for scientific data management and stewardship\" [23]. FAIR principles consist in:  
* Being Findable, registered and indexed, searchable, richly described in metadata;
* Being Accessible, through open, free, universally implementable protocols;* Being Interoperable, with appropriate standards for metadata in the context of knowledge representation;
* Being Reusable, clearly licensed, well described, relevant to a domain, and meeting community standards. Among free and open resources, several software and organized packages integrating pre-processing and data analysis workflows for neuroimaging and signal processing became the reference for worldwide researchers in neuroscience. Such tools allow us to perform scientific research in neuroscience easily in solid and repeatable ways. It can be useful to mention, for neuroimaging, Freesurfer (https://surfer. nmr.mgh.harvard.edu/) [24] and FSL ([[https://fsl.fmrib.ox.ac.uk/fsl/fslwiki](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki)]([https://fsl.fmrib.ox.ac.uk/fsl/fslwiki](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki))) [25] that are standalone softwares, and the MATLAB-connected SPM ([[https://www.fil.ion.ucl.ac.uk/spm/](https://www.fil.ion.ucl.ac.uk/spm/)]([https://www.fil.ion.ucl.ac.uk/spm/](https://www.fil.ion.ucl.ac.uk/spm/))) [26]. In the domain of signal processing, examples are EEGLAB ([[https://sccn.ucsd.edu/eeglab/index.php](https://sccn.ucsd.edu/eeglab/index.php)]([https://sccn.ucsd.edu/eeglab/index.php](https://sccn.ucsd.edu/eeglab/index.php))) [27], Brainstorm ([[https://neuroimage.usc.edu/brainstorm/Introduction](https://neuroimage.usc.edu/brainstorm/Introduction)]([https://neuroimage.usc.edu/brainstorm/Introduction](https://neuroimage.usc.edu/brainstorm/Introduction))) [28], PaWFE ([[http://ninapro.hevs.ch/node/229](http://ninapro.hevs.ch/node/229)]([http://ninapro.hevs.ch/node/229](http://ninapro.hevs.ch/node/229))) [29], all MATLAB related yet free and open, and MNE ([[https://mne.tools/stable/index.html](https://mne.tools/stable/index.html)]([https://mne.tools/stable/index.html](https://mne.tools/stable/index.html))) [30], that runs on Python. Regarding applications for neurorobotics and Brain Computer Interfaces (BCIs), a recent open source platform can be found in ROS-neuro ([[https://github.com/rosneuro](https://github.com/rosneuro)]([https://github.com/rosneuro](https://github.com/rosneuro))) [31]. All URLs accessed at date 22 nd of November 2022.