Our NeuroNex Theory Hub is designed to provide neuroscience research and neuroscience researchers with outstanding theoretical and statistical support, guidance and inspiration. The primary ways we do this is to collaborate with other researchers and to provide them with a vibrant place to come to advance these collaborations. Columbia's NeuroNex Theory Hub brings together researchers with experience in developing and applying advanced statistical and modeling methods and who are embedded in an extensive network of collaborations across the entire field of neuroscience. We do this in part through the programs listed below.
Our NeuroNex programs are sponsored by NSF award DBI-1707398.
Neurotheory Workshop Series (NeWS) at ZMBBI
The Center for Theoretical Neuroscience at Columbia University is happy to announce a series of monthly workshops in 2022 that will be held at the Zuckerman Institute for Mind Brain and Behavior (Jerome L Greene Science Center, New York). These workshops are intended for PhD students and postdocs who want to learn new and advanced techniques in modeling and data analysis. During the workshops, attendees will hear about a research project, followed by a hands-on tutorial and data-hacking sessions.
The next in the series will be:
Time: April 14th, 2022 @ 1:30pm - 4:30pm
Location: Jerome L. Greene Science Center
Thomas Donoghue: Parameterizing periodic and aperiodic activity in neural times series
Abstract: Neuro-electrophysiological data contain both periodic components, or neural oscillations, as well as prominent aperiodic (1/f-like) activity. While neural oscillations are a common feature of analysis, aperiodic neural activity is less commonly analyzed, though recent work has started to motivate it’s putative physiological interpretations and dynamics across age, physiological states, and task demands. Notably, common analysis approaches, including Fourier-based approaches and analyses that decompose the data into narrow, canonically defined, frequency bands can conflate periodic and aperiodic activity. In this workshop, we will first use simulated data and apply common methods to demonstrate and explore how standard analysis methods can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent). To address these issues, we will then introduce and work with a novel algorithm, available as an open-source Python module (‘specparam’), to parameterize neural power spectra into periodic and aperiodic features, that addresses these limitations. Through hands-on investigations, we will work on applying this method, including to resting state data and task designs in empirical datasets. We will conclude with a discussion of the implications and potential interpretations of applying spectral parameterization to study periodic and aperiodic neural activity.
In order to maximize in-depth discussions and hands-on interaction within the group, we are limiting the group size on a first come first serve basis. Refreshments will be provided. In addition, a limited budget to cover short-travel expenses (e.g., train rides from the tri-state area) is also available. We encourage participation from female scientists and those belonging to underrepresented minority groups.
2/16/2022, 1.30 pm - 4.30 pm: Amin Nejatbakhsh, Elom Amematsro
Probabilistic Modeling of Neural Data
1/22/2020, 1.30pm-5.30pm: Ella Batty and Matt Whiteway
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
2/19/2020, 1.30pm-5.30pm: Shreya Saxena
Interpretable models of multi-regional calcium imaging data
Questions? Email [email protected]
Organizers: Gabrielle Gutierrez, Amin Nejatbakhsh.
The Spring 2020 workshop series was held at Columbia University's Jerome L Green Science Center.
NeWS is funded by NSF's NeuroNex Award DBI-170739.
This program supports researchers from academic institutions outside the NYC area (NYC visitors are welcome but do not require support) to visit the Theory Hub. These visitors may be theorists or experimentalists looking to learn new techniques or to collaborate in analyzing and modeling data. To participate, please email Stefano Fusi and Allison Ong with your research interests.
Neuroscience Cloud Analysis As a Service
NeuroCAAS is an open-source scientific resource that uses cloud computing to run powerful modern data analyses. It packages these analyses into fully portable descriptions called blueprints, which can be deployed to analyze data on demand and automatically. Analyzing data with NeuroCAAS
does not require any hardware purchases, installation, or dependency management. The scientific user is only responsible for two things: 1) their data, and 2) a configuration file containing analysis parameters. We provide these services to any interested researcher free of charge.
Access the NeuroCAAS site here
Page Last Updated: 3/10/2022