Date: Friday, March 8, 2024

Time: 3:00 p.m. - 4:00 p.m.

Location: Centergy Building 5126. The associated zoom link is: https://gatech.zoom.us/j/97202569997

Speaker: Nathanael Cruzado

Speakers' Title: Georgia Institute of Technology Postdoctoral Fellow

Seminar Title: Applying more quantitative techniques to neuroscience: an in-silico demonstration

Abstract: Many common statistical and engineering tools and techniques such as control systems, Bayesian optimization, maximum likelihood parameter estimation, and model selection are currently underutilized in neuroscience, although recent advances have started to change that.  Using an in-silico (i.e. simulated) paradigm I demonstrate how these tools can be used in neuroscience to enable better, more quantitative approaches to setting up experiments, analyzing neural spiking data, and comparing neural models.  I demonstrate this more quantitative approach by comparing two example neural circuit models.  In my talk I will walk through this process from end-to-end: setting up the models in an in-silico experimental paradigm to obtain simulated data, applying control system techniques and Bayesian optimization to probe the models, using maximum likelihood parameter estimation on the simulated data, and finally using model selection to adjudicate between the models.

Bio: Nathanael Cruzado is a Post Doctoral Fellow with the School of Electrical and Computer Engineering in the Structured Information for Precision Neuroengineering Lab (SIPlab) at Georgia Tech.  He received his PhD in Computational Neuroscience in 2021 at Boston University advised by Marc Howard and his bachelor's degree in electrical engineering and mathematics in 2015 at Auburn University.  His work includes neural data analysis, neural modeling, cognitive modeling, and closed-loop control of neural systems.