Towards the Neuroethology of Vocal Communication in the Mongolian Gerbil

Описание: Presented By: Alex Williams, PhD

Speaker Biography: Alex is jointly appointed as an Assistant Professor in the Center for Neural Science at NYU and an Associate Research Scientist / Project Leader at the Flatiron Institute. Alex develops statistical models to characterize functional flexibility in large-scale neural circuits—e.g., how the dynamics of large neural ensembles change when learning a new skill, during periods of high attention or task engagement, or over the course of development and aging. Alex performed his postdoctoral work in the Statistics Department at Stanford University working with Scott Linderman’s research group. Before that, he obtained a PhD in Neuroscience from Stanford with supervision from Surya Ganguli. He has also worked at Google Brain (with David Sussillo), Sandia National Labs (with Tamara Kolda), the Salk Institute (with Terry Sejnowski), and Brandeis University (with Eve Marder) as a visiting researcher / technician.

Webinar: Towards the Neuroethology of Vocal Communication in the Mongolian Gerbil

Webinar Abstract: Social animals congregate in groups and communicate with vocalizations. To study the dynamics of natural vocal communication and their neural basis, one must characterize signals used for communication and determine the sender and receiver of the signal [1]. To this end, we established two complementary approaches for (1) quantifying vocal repertoire using a variational autoencoder (VAE) with longitudinal audio recordings in a naturalistic social environment, and (2) vocal call attribution using a deep neural network. We pursued this research by establishing a unique and favorable model organism — the Mongolian gerbil — which has a sophisticated vocal repertoire and complex social hierarchy, including pair bond formation [2]. Here, we made continuous acoustic recordings of three separate gerbil families for 20 days each, and used a VAE for unsupervised representation learning of acoustic features to show that gerbil families have family specific vocal repertoires. Although this result positions the gerbil as an intriguing model of social vocal interactions, the inability to attribute vocalizations to individuals in a group limits interpretability of these family vocal differences, and remains a persistent problem for others in the field. We have therefore developed (1) a supervised deep learning framework with calibrated uncertainty estimates that achieves state-of-the-art sound source localization performance, (2) novel hardware solutions to generate benchmark datasets for training/evaluating sound source localization models across labs, and (3) curated and released the first large-scale benchmark datasets for vocal call localization in social rodents.

Earn PACE Credits:
1. Make sure you’re a registered member of Labroots (https://www.labroots.com/)
2. Watch the webinar on YouTube or on the Labroots Website (https://www.labroots.com/ms/webinar/t...)
3. Click Here to get your PACE credits (Expiration date – March 06, 2026): (https://www.labroots.com/credit/pace-...

Labroots on Social:
Facebook:   / labrootsinc  
Twitter:   / labroots  
LinkedIn:   / labroots  
Instagram:   / labrootsinc  
Pinterest:   / labroots  
SnapChat: labroots_inc