Simons-Emory Theory Methods Workshop

May 19th-21st from 10am-2pm EDT on Zoom

Symposium registration here.

The Simons-Emory International Consortium on Motor Control is focused on the development and application of new experimental and computational technology to the study of movements during natural behavior, and an essential component of our efforts is to disseminate these approaches to a wide audience of interested scientists. The goal of this workshop is to provide a practical introduction for graduate students and postdocs to the computational tools that form the basis of the Consortium’s efforts (see below for details). To promote a more direct teaching atmosphere, we anticipate limiting participation to 100 individuals. A zoom link will be sent-out to all registered participants about a week before the workshop.

We plan on devoting one day for each of the three topic areas (see “Workshop References” below for citations). On Day 1 (5/19), Gordon Berman’s group will discuss methods for quantifying, analyzing, and modeling animal behavior in the context of neuroscientific experiments. On Day 2 (5/20), Chethan Pandarinath’s group will discuss analyzing and denoising the latent dynamics of neural populations and EMG data using techniques from deep learning. Lastly, on Day 3 (5/21), Ilya Nemenman’s group will discuss methods of estimating mutual information and discovering important patterns in single unit spiking data.

Each day will consist of four primary sections:

1. A 30-minute presentation (from the PI) on the motivation/concepts underlying the methods

2. A 30-minute presentation (from a trainee in the group) on the specifics of the methods and a brief tutorial on how to use the method and the associated software

3. A 2-hour open-ended tutorial, where the participants will get hands-on experience using the methods and the associated software, assisted by trainees from the Consortium. Data will be provided, but some student may choose to analyze data from their own research

4. A 30-minute discussion about lessons learned during the tutorial and about common problems, technicalities, difficulties

Day 1: Gordon Berman,
TAs: Sena Agezo and Kanishk Jain

- Berman, Gordon J., Daniel M. Choi, William Bialek, and Joshua W. Shaevitz. "Mapping the stereotyped behaviour of freely moving fruit flies." Journal of The Royal Society Interface 11, no. 99 (2014): 20140672.

- Berman, Gordon J., William Bialek, and Joshua W. Shaevitz. "Predictability and hierarchy in Drosophila behavior." Proceedings of the National Academy of Sciences 113, no. 42 (2016): 11943-11948.

- Cande, Jessica, Shigehiro Namiki, Jirui Qiu, Wyatt Korff, Gwyneth M. Card, Joshua W. Shaevitz, David L. Stern, and Gordon J. Berman. "Optogenetic dissection of descending behavioral control in Drosophila." Elife 7 (2018): e34275.

- Saravanan, Varun, Gordon J. Berman, and Samuel J. Sober. "Application of the hierarchical bootstrap to multi-level data in neuroscience." Neurons, Behavior, Data Analysis, and Theory (2020).

Day 2: Chethan Pandarinath,
TAs: Mattia Thompson and Lahiru Wimalasena

- Pandarinath, Chethan, Daniel J. O’Shea, Jasmine Collins, Rafal Jozefowicz, Sergey D. Stavisky, Jonathan C. Kao, Eric M. Trautmann et al. "Inferring single-trial neural population dynamics using sequential auto-encoders." Nature methods15, no. 10 (2018): 805-815.

- Keshtkaran, Mohammad Reza, Andrew Robert Sedler, Raeed H. Chowdhury, Raghav Tandon, Diya Basrai, Sarah L. Nguyen, Hansem Sohn, Mehrdad Jazayeri, Lee E. Miller, and Chethan Pandarinath. "A large-scale neural network training framework for generalized estimation of single-trial population dynamics." bioRxiv: 10.1101/2021.01.13.426570 (2021).

- Ye, Joel and Panparinath, Chethan “Representation learning for neural population activity with Neural Data Transformers” bioRxiv: 10.1101/2021.01.16.426955 (2021).

Day 3: Ilya Nemenman,
TAs: Damián Hernández and Caroline Holmes

- Srivastava, Kyle H., Caroline M. Holmes, Michiel Vellema, Andrea R. Pack, Coen PH Elemans, Ilya Nemenman, and Samuel J. Sober. "Motor control by precisely timed spike patterns." Proceedings of the National Academy of Sciences114, no. 5 (2017): 1171-1176.

- Hernández, Damián G., Samuel J. Sober, and Ilya Nemenman. "Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds." arXiv preprint arXiv:1911.08509 (2019).

- Holmes, Caroline M., and Ilya Nemenman. "Estimation of mutual information for real-valued data with error bars and controlled bias." Physical Review E 100, no. 2 (2019): 022404