Research Intern, Gulati Lab at Cedars-Sinai in Los Angeles, CA

This internship provides a structured, hands-on educational experience in systems neuroscience, rodent behavioral analysis, and foundational neurophysiology and histological techniques. The role is designed to introduce the intern to the full experimental workflow used in the lab, while fostering collaborative research and developing strong technical and analytical skills.

Key Responsibilities:

• Receive comprehensive training in rodent handling, behavioral task shaping and training, and neurophysiological recording procedures.
• Learn post-euthanasia perfusion methods and brain tissue processing, including slicing, mounting, and imaging, to assess stroke injury and verify neural probe placement.
• Apply machine learning-based methods for automated kinematic analysis of rodent reaching behavior, including pose estimation and extraction of movement features.
• Learn introductory spike sorting workflows for processing extracellular neurophysiological data and linking neural activity to behavior.
• Conduct quantitative analyses of motor behavior in the context of stroke recovery.
• Perform spike sorting to extract and evaluate neural firing patterns.
• Utilize imaging methods to measure stroke volume and confirm probe placement.
• Support ongoing experimental studies with both wet lab and computational skills.

Required Qualifications:

• Currently enrolled in or a recent graduate of an undergraduate or graduate program in Neuroscience, Biology, Psychology, Computer Science, or a related field.
• Strong interest in systems neuroscience and animal research.
• Basic familiarity with programming concepts, preferably in Python.
• Excellent attention to detail and organizational skills.
• Ability to work both independently and as part of a collaborative team.
• Willingness to work with rodents and follow all animal care and use protocols.

Preferred Qualifications:

• Prior experience with animal handling or in a wet lab environment.
• Experience with data analysis, machine learning, or computational modeling.
• Familiarity with neurophysiology or behavioral analysis techniques.

Learning Objectives:
By the end of the internship, the intern will be able to:

• Apply machine learning tools for automated behavioral and kinematic analysis.
• Conduct quantitative analyses of motor behavior in the context of stroke recovery.
• Perform spike sorting to extract and evaluate neural firing patterns.
• Utilize imaging methods to measure stroke volume and confirm probe placement.
• Support ongoing experimental studies with both wet lab and computational skills.

Internship Timeline & Activities:

Weeks 1-2: Onboarding & Foundational Training

• Complete all institutional trainings, IACUC/BAR requirements, and laboratory safety orientation.
• Learn basic rodent handling, husbandry, and health monitoring.
• Introduction to behavioral equipment, electrophysiology rigs, and recording workflow.
• Set up computational environment (Python, DeepLabCut, spike sorting tools) and receive an overview of data organization, video processing pipelines, and analysis workflow.

Weeks 2-5: Pre-Stroke Behavioral Training & Data Collection

• Begin handling and behavioral shaping on new animals.
• Determine handedness, operate automated reach training system, and acquire behavioral videos.
• Organize raw videos and begin basic preprocessing for machine learning-based pose estimation.

Weeks 6-9: Post-Stroke Behavioral & Electrophysiology Studies

• Conduct post-stroke behavioral sessions and electrophysiological recordings.
• Receive structured training in DeepLabCut model training, pose extraction, and kinematic feature analysis.
• Learn spike sorting workflows, including detection, clustering, quality control, and initial linking of neural activity to behavior.

Weeks 10-12: Histology & Project Completion

• Participate in transcardial perfusion, tissue collection, slicing, mounting, and Nissl staining.
• Evaluate stroke lesion extent and confirm probe placement.
• Finalize machine learning kinematic analyses and spike sorted datasets for end-of-term review.

Qualifications