CNS 2020 Workshop9 Part2
Channel: OCNS
Date: 2020-07-31

CNS2020 Workshop 9, Part 2, 7/22/20

https://sites.google.com/view/cns-ml-msm-workshop-2020/home

Breakthrough technology developments in semi-automated, high-throughput data collection have enabled experimental neuroscientists to acquire more multiscale neural data than ever before. However, the neural origin of the patterns observed in the multiscale, multimodal datasets are often difficult to decipher. There is therefore a critical need for time- and cost-efficient approaches to analyze and  interpret the massive datasets to advance understanding of cellular and circuit-level origins of the observed neural dynamics in both health and disease, and to use the insights gained to develop new therapeutics. While machine learning is a powerful technique to integrate multimodal data, classical machine learning  techniques often ignore the fundamental laws of physics and may therefore result in non-physical solutions.  Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and unravel mechanisms that  explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets  from different sources and different levels of resolution. This workshop aims to highlight research that bridges the disciplines of machine learning and multiscale modeling. Speakers are invited to address open questions, and discuss potential challenges and  limitations in several topical areas: differential equations, data-driven  approaches, and theory-driven approaches. This multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can  provide new insight into disease mechanisms, help identify new targets or treatment strategies, and inform decision  making in the benefit of human health.