<p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedical domain. Self-Supervised Learning (SSL) has emerged as a prominent solution for these problems, as it allows to learn powerful data representations in an unsupervised manner. Despite most applications in biomedical research targeted images, the high amount of recent works targeting biosignals can make it difficult for researchers to have a complete picture of the current situation. The aim of this paper is to outline and clarify the state of the art in the domain. The article briefly summarizes the nature and acquisition modality of biomedical signals, introduces the SSL method, and provides a complete but synthetic overview of the main works applying SSL for the analysis of biomedical signals. The analysis of the scientific literature highlights the importance of SSL, confirming its potential to improve the integration of deep learning into clinical tasks. </p>

<p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedical domain. Self-Supervised Learning (SSL) has emerged as a prominent solution for these problems, as it allows to learn powerful data representations in an unsupervised manner. Despite most applications in biomedical research targeted images, the high amount of recent works targeting biosignals can make it difficult for researchers to have a complete picture of the current situation. The aim of this paper is to outline and clarify the state of the art in the domain. The article briefly summarizes the nature and acquisition modality of biomedical signals, introduces the SSL method, and provides a complete but synthetic overview of the main works applying SSL for the analysis of biomedical signals. The analysis of the scientific literature highlights the importance of SSL, confirming its potential to improve the integration of deep learning into clinical tasks. </p>

<p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedical domain. Self-Supervised Learning (SSL) has emerged as a prominent solution for these problems, as it allows to learn powerful data representations in an unsupervised manner. Despite most applications in biomedical research targeted images, the high amount of recent works targeting biosignals can make it difficult for researchers to have a complete picture of the current situation. The aim of this paper is to outline and clarify the state of the art in the domain. The article briefly summarizes the nature and acquisition modality of biomedical signals, introduces the SSL method, and provides a complete but synthetic overview of the main works applying SSL for the analysis of biomedical signals. The analysis of the scientific literature highlights the importance of SSL, confirming its potential to improve the integration of deep learning into clinical tasks. </p>

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.