In this paper, we propose an online multi-spectral neuron tracing method with uniquely designed modules, where no offline training are required. Our method is trained online to update our enhanced discriminative correlation filter to conglutinate the tracing process. This distinctive offline-training-free schema differentiates us from other training-dependent tracing approaches like deep learning methods since no annotation is needed for our method. Besides, compared to other tracing methods requiring complicated set-up such as for clustering and graph multi-cut, our approach is much easier to be applied to new images. In fact, it only needs a starting bounding box of the tracing neuron, significantly reducing users' configuration effort. Our extensive experiments show that our training-free and easy-configured methodology allows fast and accurate neuron reconstructions in multi-spectral images.

Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. To guarantee that female athletes receive the optimal care they deserve, researchers must employ advanced neuroimaging techniques and sophisticated machine-learning models. These tools enable an in-depth investigation of the underlying mechanisms responsible for concussion symptoms stemming from neuronal dysfunction in female athletes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions.

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.

This special issue publishes research studies on the advances in the field of computing and artificial intelligence and collects state-of-the-art contributions on the latest research and development and challenges in the field of Medical Informatics and Biomedical Image Processing for the analysis and exploration of the nervous system. We hope to receive innovative contributions in both theoretical and practical aspects. Strong emphasis is placed on innovative results in theory, methodology and applications of artificial intelligence. Topics may be related to computer vision and image understanding, machine learning, search techniques, medical image or data analysis, and use of relevant specialized hardware/software architectures. Papers must be submitted online to Neuroscience Informatics on the online submission website Editorial Manager by August, 31 2022 to be considered and accepted by October 4, 2022.

As we move towards more ubiquitous, always-on sensing and computing, power becomes increasingly important. There's perhaps no better an example of where this is important than the voice-activated devices on our desks, in our pockets, and distributed around our homes. As we saw last year, keyword spotting in particular is currently a target for all kinds of neuromorphic technologies. The 2020 winner of the Misha Mahowald Prize for Neuromorphic Engineering is Prof. Shih-Chii Liu and her team, who have been working on low-latency, low-power sensors for detecting speech. The dynamic audio sensors that Shih-Chii Liu and her team at the Institute of Neuroinformatics (INI) have been developing could eventually address this market.

Though parts of the world have succeeded in suppressing the coronavirus and are now opening up, it will be some time before we can start traveling to conferences again. I was supposed to attend two meetings this spring and then the Telluride Neuromorphic Engineering Workshop this summer. I enjoy poring through the literature, but was looking forward to hearing from the researchers themselves. So I decided to console myself by putting together a list of (mostly) recent technical neuromorphic video talks available online and have shared these with the neuromorphic community (and now with you). I find conference presentations a much better way into new subject matter than papers: you get a context, explanation, and overview without being bogged down with technical details.

Google's autonomous cars may look cute, like a yuppie cross between a Little Tikes Cozy Coupe and a sheet of flypaper, but to make it in the real world they're going to have to act like calculating predators. At least, that's what a handful of scientists at the Institute of Neuroinformatics at the University of Zurich in Switzerland believe. They recently taught a robot to act like a predator and hunt its prey--which was a human-controlled robot--using a specialized camera and software that allowed the robot to essentially teach itself how to find its mark. The end goal of the work is arguably more beneficial to humanity than creating a future robot bloodsport, however. The researchers aim to design software that would allow a robot to assess its environment and find a target in real time and space.

Cameras and audio equipment are getting better all the time, but mostly through brute force: more pixels, more sensors, and better post-processing. Mammalian eyes and ears beat them handily when it comes to efficiency and the ability to only focus on what's interesting or important. Neuromorphic engineers, who try to mimic the strengths of biological systems in manmade ones, have made big strides in recent years, especially with vision. Researchers have made machine-vision systems that only take pictures of moving objects, for example. Instead of taking many images at a steady, predetermined rate, these kinds of cameras monitor for changes in a scene and only record those.

The data perspective can be found in Huerta for Neuroscience, for example, has can be stored in robust databases and and Koslow (1996).