Neuroscience Gateway - Modeling, Data Analysis and Software Dissemination on Supercomputers

Satellite Workshop - 2024 Society for Neuroscience Annual Meeting, Chicago, IL

Date and Time: Saturday, October 5, 2024, 08:30 AM - 11:30 AM (US Central Time)

Location: Social Sciences Research Building (Social Sciences Common Room (SSRB 201))

1126 East 59th Street

University of Chicago

Chicago, IL 60637

https://maps.uchicago.edu/

 

Registration (is free but is required given the limited space, please register here): https://na.eventscloud.com/2024.10-nsg-workshop

 

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

Workshop theme: The Neuroscience Gateway (NSG) serves the neuroscience community by providing researchers and students easy, open and free access to a large number of neuroscience software and tools on supercomputing resources, academic cloud computing resources, and associated storage resources, which are located at various national academic supercomputer centers in the US. NSG can be used by neuroscientists from any country. NSG eliminates administrative and technical barriers for all researchers and students and enables neuroscientists to carry out research that requires large scale modeling and data processing using data from neuroscience data archives. NSG enables dissemination of neuroscience modeling and data processing software to the community. This workshop will bring together researchers and educators from the broader neuroscience community and they will share their perspective on neuroscience research, education and broad outreach as it relates to computing, data archives, data sharing, training and teaching.

AGENDA

8:30 AM - 8:45 AM

Welcome

8:45 AM - 9:15 AM

Title: Human Neocortical Neurosolver: A Software Tool for Cell and Circuit Level Interpretation of MEG/EEG signals

Authors: Nicholas Tolley, Stephanie Jones

Affiliation: Brown University

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.

9:15 AM - 9:45 AM

Title: The NEMAR Neuromagnetic Data, Tools, and Compute Resource

Authors: Scott Makeig1, Kenneth Yoshimoto2, Choonhan Youn2, Dung Troung1, Subhashini Sivagnanam2, Amitava Majumdar2, Arnaud Delorme1

Affiliation: 1Swartz Center for Computational Neuroscience, 2San Diego Supercomputer Center, University of California San Diego

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.

 

9:45 AM - 10:15AM

Title: DANDI: Building a collaborative ecosystem for neuroscientific data

Authors: Satrajit Ghosh

 

Affiliation: McGovern Institute, MIT

 

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.

 

10:15 AM - 10:30 AM BREAK

 

10:30 AM - 11:00 AM

Title: Enhancing Perspectives in Neuroscience Research through Diverse Institutional Partnerships.

Author: Elba Serrano

 

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.

 

11:00 AM - 11:30 AM

Title: Bio-realistic modeling of the mouse primary visual cortex using large-scale datasets

Authors: Shinya Ito1, Darrell Haufler1, Kael Dai1, Joe Aman1, Javier Galván Frail2, Guozhang Chen3, Claudio Mirasso2, Wolfgang Maass4, Anton Arkhipov1

 

Affiliation:

1. Allen Institute, Seattle, Washington, USA

2. IFISC, University deles Illes Balears, Palma de Mallorca, Spain

3. Peking University, Beijing, China

4. Graz University of Technology, Graz, Austria

 

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.