High-Dimensional Data Analysis Laboratory

Projects

The central goal of this project is to access the quality and performance of the existent and emerging machine learning (ML) and artificial intelligence (AI) approaches with respect to their ability to describe, to explain and to predict the neuronal behaviour on the basis of lab-data from mouse experiments. More common ML and AI approaches (hidden Markov models, shallow and reinforced learning, machine learning) will be compared to the very recently-developed Scalable Probabilistic Approximation approaches (Gerber et al., Sci. Adv. 2020) and to the entropy-driven approaches. Results of these comparison will aim at identifying the simplest possible (but not simpler then necessary) models that provide the most adequate lab-data descriptions. Identification of such models will enhance our understanding of emergence in the neuronal activity and provide a guidance for further experiments.

People

Horenko I. (Responsible)
Group
High-Dimensional Data Analysis Laboratory (Prof. Horenko)
Start Date
01.01.2021
End Date
31.12.2023
Duration
24 Months
Funding Sources
Carl Zeiss Foundation
Status
Active
Category
Carl Zeiss Foundation