BIOMSC X903 - Omics 4 Neuro - AI in Neuroscience: from Omics to Virtual cells, Image and Behavior analyses
This three-day module introduces participants to the application of artificial intelligence and machine learning in neuroscience and the life sciences. Day 1 provides a conceptual foundation spanning the progression from machine learning to deep learning, generative AI and foundation models, and emerging approaches such as virtual cell modeling. This will be contextualized through afternoon case studies on AI applications in neuroscience, multimodal data, and a guided hands-on activity on deep learning image segmentation. Days 2 and 3 shift to applied practice, guiding participants through an AI-driven behavioral analysis pipeline that includes markerless pose estimation using DeepLabCut and behavioral clustering using B-SOiD and/or Keypoint Moseq, culminating in the ability to execute an end-to-end AI behavioral analysis workflow from raw video data to behavioral insight.
The module is intended for graduate students, postdoctoral researchers, staff, and faculty seeking a working understanding of AI and machine learning approaches as applied to neuroscience and omics, imaging, and behavioral data. No prior coding required: hands-on activities utilize desktop graphical interfaces (GUIs).
Delivery Options
Hybrid: A learning experience that combines online coursework with in-person instruction, activities, or events. Participants complete designated online learning components and attend scheduled in-person sessions.
Time to Completion
This program is projected to take approximately 18 hours to complete.
Course Completion Requirements
Successful completion of this course requires earning a score of at least 99% on required assessments.
Technology Requirements
- Reliable internet connection
- Computer, laptop, or tablet capable of accessing online course materials
- Ability to stream video and interactive content
- Recommended browsers: Google Chrome or Firefox
Additional Technology Information
A laptop capable of running DeepLabCut, B-SOiD and/or Keypoint Moseq, and Segmentation Factory. Example images and videos will be provided, or you are welcome to bring your own experimental data.
Prerequisite Requirements
No prior AI/ML experience required for Day 1. Participants should have DeepLabCut, B-SOiD and/or Keypoint Moseq, and Segmentation Factory installed and able to open prior to the workshop; a TA-led installation help session will be offered one week in advance for anyone who needs assistance.
What You’ll Learn
- Be able to speak knowledgeably about the broader AI landscape in biology.
- Construct an end-to-end pipeline from raw video to behavioral insight.
- Critically evaluate where AI-based approaches help and where their limitations matter.
What You’ll Earn
- A certificate of completion from Ohio State University College of Medicine.
Additional Information
Please note that tools should be accessed prior to the start of the in-person workshop. For assistance, reach out to Charlie.Campbell@osumc.edu.
Contact Us
For questions about the enrollment process, payment options, technology, or course records, please submit an inquiry and our Enrollment Support team will assist you within 24 hours during business days.
For questions about course details email COM_CCE@osumc.edu.