Webinar Overview
Modeling complex neurodegenerative, neuropsychiatric, and neurological diseases in animals requires reliable in vivo readouts and endpoints. However, complex behavior data analysis can be cumbersome. Relying on manual and subjective human observation can be time consuming and have negative impacts on reproducibility rates.
New applications of machine learning are changing how we approach CNS data capture and analysis. Recent advancements show that it can improve reproducibility, maximize the value of your data, and reduce the number of animals and amount of time required for data analysis.
In this webinar in vivo scientists and data analysis experts will:
- Guide you through the development of machine learning algorithms for tracking and classification of behaviors
- Provide case study examples of quantification of social and isolated behaviors
- Share how machine learning can be customized to specific study requirements
Webinar Presenters
Johanna Uhari-Väänänen, PhD
In Vivo Scientist
Charles River
Riccardo De Feo, PhD
Data Scientist
Charles River
Timo Bragge, MSc
Principal Data Scientist
Charles River
Scientific Moderator
Jussi Rytkönen, PhD
Principal In Vivo Scientist
Charles River