Open Source Software seminars

OpenSPM aims to democratize innovation in the field of scanning probe microscopy (SPM), which is currently dominated by a few proprietary, closed systems that limit user-driven development. Our platform includes a high-speed OpenAFM head and base optimized for small cantilevers, an OpenAFM controller, a high-voltage amplifier, and interfaces compatible with several commercial AFM systems such as the Bruker Multimode, Nanosurf DriveAFM, Witec Alpha SNOM, Zeiss FIB-SEM XB550, and Nenovision Litescope. We have created a fully documented and community-driven OpenSPM platform, with training resources and sourcing information, which has already enabled the construction of more than 15 systems outside our lab. The controller is integrated with open-source tools like Gwyddion, HDF5, and Pycroscopy. We have also engaged external companies, two of which are integrating our controller into their products or interfaces. We see growing interest in applying parts of the OpenSPM platform to related techniques such as correlated microscopy, nanoindentation, and scanning electron/confocal microscopy. To support this, we are developing more generic and modular software, alongside a structured development workflow. A key feature of the OpenSPM system is its Python-based API, which makes the platform fully scriptable and ideal for AI and machine learning applications. This enables, for instance, automatic control and optimization of PID parameters, setpoints, and experiment workflows. With a growing contributor base and industry involvement, OpenSPM is well positioned to become a global, open platform for next-generation SPM innovation.

Edmund will present why to use FPGAs when building scientific instruments, when and why to use open source FPGA tools, the history of their development, their development status, currently supported FPGA families and functions, current developments in design languages and tools, the community, freely available design blocks, and possible future developments.

Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behaviors or functions is disputed (Weber et al., 2010; Siddiqiet al 2022). On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain (Price, 2018). Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (e.g., Larivière et al., 2021). We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR. We call it neurocausal (https://neurocausal.github.io).

FED3 is a device for behavioral training of mice in vivarium home-cages. Mice interact with FED3 through two nose-pokes and FED3 responds with visual stimuli, auditory stimuli, and by dispensing pellets. As it is used in the home-cage FED3 can be used for around-the-clock training of mice over several weeks. FED3 is open-source and can be built by users for ~10-20x less than commercial solutions for training mice. The control code is also open-source and was designed to be easily modified by users.

Modern biological methods often require a large number of experiments to be conducted. For example, dissecting molecular pathways involved in a variety of biological processes in neurons and non-excitable cells requires high-throughput compound library or RNAi screens. Another example requiring large datasets - modern data analysis methods such as deep learning. These have been successfully applied to a number of biological and medical questions. In this talk we will describe an open-source platform allowing such experiments to be automated. The platform consists of an XY stage, perfusion system and an epifluorescent microscope with autofocusing. It is extremely easy to build and can be used for different experimental paradigms, ranging from immunolabeling and routine characterisation of large numbers of cell lines to high-throughput imaging of fluorescent reporters.