We are organizing a workshop in Chicago on October 5^th , 2024 starting at 8:30 am (Central time) and till 11:30am (Central time) at the campus of University of Chicago. The agenda is provided below. If you are interested in attending, please email Amit Majumdar (amajumdar@ucsd.edu) to receive the link to register. Registration is free but due to limited seatings, we are requesting registration and it will also provide the address of the location of the workshop.

Abstract: MEG/EEG signals are correlated with nearly all healthy and pathological brain functions. However, it is still extremely difficult to infer the underlying cellular and circuit level origins. This limits the translation of MEG/EEG signals into novel principles of information processing, or into new treatment modalities for pathologies. To address this limitation, we built the Human Neocortical Neurosolver (HNN): an open-source software tool to help researchers and clinicians without formal computational modeling or coding experience interpret the neural origin of their human MEG/EEG data. The foundation of HNN is a biophysically-detailed neocortical model, representing a patch of neocortex receiving thalamic and corticocortical drive. The HNN model can be accessed through a user-friendly interactive graphical user interface (GUI) or through a Python scripting interface. Tutorials are provided to teach users how to begin to study the cell and circuit level origin of sensory event related potentials (ERPs) and low frequency rhythms. The package is available to install with a single command on PyPI (pip install hnn_core), is unit tested and extensively documented. HNN is additionally accessible through computing resources offered by the Neuroscience Gateway (NSG) enabling large simulation workloads. We will give an overview of the background of HNN, describe the newest features added to the software, and highlight recent research projects using HNN.

Abstract: The recent BRAIN Initiative, funded by the Obama administration, propelled the creation of archives of publicly and other funded scientific data of all types. For human functional neuroimaging, the OpenNeuro.org archive was funded to collect and publicly share data of all types. Its creator, Russ Poldrack, is an fMRI expert. For other imaging modalities, NIMH funded projects to curate data contributed to OpenNeuro. Our NEMAR.org serves that purpose for 'neuroelectromagnetic' data (EEG, MEG, iEEG). Beyond simple data curation, publication of data quality measures and data visualization, NEMAR exemplifies what I believe should become the basic unit of open science, what I call the 'integrated data, tools, and compute resource' (datcor). By teaming with the Neuroscience Gateway team, NEMAR now supports users worldwide in identifying and performing sophisticated computations on increasing amounts of publicly available data - without need for at-best balky data downloads.

Abstract: The DANDI Archive is a community-oriented platform designed to support the sharing, analysis, and re-use of neurophysiology and microscopy data, using the BRAIN Initiative supported standards for data sharing. By providing an open, FAIR-compliant (Findable, Accessible, Interoperable, Reusable) ecosystem, DANDI facilitates collaborative research in neuroscience. It integrates diverse data types, from electrophysiology to imaging, ensuring that researchers can contribute, discover, access, visualize, and compute on standardized datasets seamlessly. This presentation will highlight the key features of DANDI, including its data management infrastructure, open-source tools, and how it promotes transparency, reproducibility, and interdisciplinary collaboration in neuroscience research.

Affiliation: New Mexico State University

Abstract: Diversity in collaboration is expected to yield more inclusive and representative research outcomes and potentially address more varied needs within a field. By pooling knowledge and resources from different disciplines, institutions, and investigators, researchers can approach problems in neuroscience from multiple angles, leading to more comprehensive and innovative solutions. The nation's 700+ federally designated minority serving institutions (MSIs) comprise about 15% of all degree-granting institutions and educate over 5 million students. This presentation will introduce attendees to the rich constellation of MSIs with a spotlight on Hispanic serving institutions (HSIs), where 65% of the nation's Latino students seek degrees. Drawing on experiences as lead for the NSF HSI National STEM Resource Hub, the speaker will provide an overview of the benefits and challenges in developing collaborations with colleagues at MSIs, as well as strategies for identifying partners and developing authentic relationships that further neuroscience research.

Abstract: Accurate models of cortical circuits facilitate a deeper understanding of how neural dynamics are shaped and maintained within the brain. We have developed an enhanced, biologically realistic model of the mouse primary visual cortex (V1), building on the framework established by Billeh et al. (Neuron, 2020). This updated model integrates new synaptic physiology data from Campagnola, Seeman et al. (Science, 2022; portal.brain-map.org/connectivity/synaptic-physiology) and detailed connectomics from the IARPA MICrONS dataset (www.microns-explorer.org), refining its connectivity and synaptic dynamics. The resulting model exhibits stable activity patterns before optimization.

Moreover, we utilized TensorFlow-based optimization techniques to align model parameters with physiological data, including Neuropixels recordings, achieving key empirical targets such as firing rates and orientation selectivity. This improved model not only enhances our understanding of cortical processing but will also be made publicly available to support further research.

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Organizers: Amit Majumdar, Subhashini Sivagnanam, Kenneth Yoshimoto

San Diego Supercomputer Center, University of California San Diego

Ted Carnevale, Neuroscience Department, Yale School of Medicine, Yale University

Co-organizers: Kimberly Grasch and H. Birali Runesha, University of Chicago

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