Biologically detailed models are essential tools in neuroscience, and automated methods are frequently used to build and validate such models based on experimental data. The open-source parameter optimization software Neuroptimus was developed to enable easy application of advanced parameter optimization methods, such as evolutionary algorithms and swarm intelligence, to various problems in neuronal modeling. Neuroptimus includes a graphical user interface, and works on various platforms, including PCs and supercomputers. While Neuroptimus uses various built-in cost functions and the eFEL feature extraction library to compare model behavior to experimental data, it severely limits the range of neuronal behaviors that can be targeted by optimization. However, the popular model-testing framework SciUnit allows for the implementation of tests that quantitatively evaluate arbitrary model behaviors. Therefore, the goal of this project is to extend Neuroptimus to use HippoUnit, an open-source neuronal test suite based on SciUnit, to evaluate model performance during optimization. This project aims to develop a seamless integration between HippoUnit and Neuroptimus, enabling the construction of detailed biophysical models of hippocampal neurons. The new Neuroptimus-HippoUnit integration will allow the optimization of a broader range of neuronal behaviors, which can ultimately lead to improved biophysical models of hippocampal neurons.

Activity patterns of neurons have so far been examined both singly and in pairs. Higher order interactions (HOIs), or non-pairwise interactions between neurons, are being studied in an effort to improve analysis. The type and quantity of information stored in groups (pairs or multiplets) in the brain is revealed by information about organizational structure (o-info). Various methods to optimize the o-info implementations in NumPy and Jax, as well as techniques for plotting the HOIs and integrating them with Frites will be included.

To promote transparency and enable the reuse of the data for subsequent studies, neuroscientists are increasingly making their experimental datasets available to the public. However, these dataset are often not in an accessible format, which makes it difficult for other researchers to carefully review and analyse the data. To address this issue, the Neurodata Without Borders (NWB) initiative is developing a format with the aim of making this data more reviewable and easier to interpret, analyse and share. The aim of this project is to convert publicly available neuroscience datasets to NWB format, add structured metadata and annotations, and make the converted datasets through the NWB Explorer on the Open Source Brain repository. This helps in aiding fellow researchers by bridging the gap between data availability and data analysis. Ultimately, the availability of well-organised and accessible neuroscience data can accelerate scientific progress and lead to better understanding and treatment of neurological disorders.

The Human Neocortical Neurosolver (HNN) is open-source, computational neural modeling software that allows us to examine the cellular- and circuit-level basis of brain responses. HNN requires the hand-tuning of a large set of parameters until a close fit between simulated and recorded data is attained. This hand-tuning can take a substantial amount of effort thus it is in the user’s best interest to automate the process so that parameters can be optimized in a time efficient manner. The goal of this project is to develop optimization functions that will perform a wide search over the parameter space to arrive at faithful simulations. HNN is currently being used to develop or test hypotheses about underlying circuitry that gives rise to cognitive processes of interest. Developing a robust algorithm for parameter optimization has the potential to illuminate avenues for the diagnosis and treatment of multiple brain disorders and diseases, cognitive impairment, and psychiatric disorders. Finally, contribution to HNN’s codebase will aid researchers who use the tool in yielding important constraints to the development of theories about the origins of human brain responses.

The Turing Way is an open source, open collaboration and community-led handbook on data science. The book is hosted online in a browsable format. Over the last four years, the book has grown significantly, making it challenging to navigate. The team created a Python package in 2022 to enhance The Turing Way's usability by enabling various access points to the book depending on the user profile or persona. But before integrating the feature provided by the package to Turing Way, there are some improvements that need to be made to allow a better user experience. The project aims at improving the Python Package to enhance the accessibility of The Turing Way.

This project aims to extend Pydra's worker classes to handle a wider range of systems by utilizing PSI/J, which offers a unified API for various schedulers. The objective of this project is to create a Pydra worker class capable of submitting and monitor jobs on different HPC systems, such as Theta, using PSI/J. Furthermore, the contributor intends to use Bokeh to construct an interactive web-based dashboard that will monitor the jobs being submitted and executed by the Pydra worker. The expected outcome of this project is a Pydra worker that can use PSI/J to submit and track jobs on various HPC systems, an updated Dask worker, as well as a jobs monitoring dashboard. The proposed methodology involves designing and implementing the Pydra worker class, evaluating its functionality and performance on various systems, comparing it to existing Pydra workers, and constructing a Pydra dashboard using Bokeh to monitor the jobs. Finally, the contributor will document the usage and installation of the Pydra worker with PSI/J, as well as the Pydra dashboard, and provide users with examples and tutorials.