NeuroTrailblazers Training Modules
Orientation to scientific thinking, growth mindset, and curiosity-driven inquiry.
Unwritten norms in science, research roles, and building confidence.
Intro to coding in Python, Jupyter notebooks, and tools for analysis.
Understanding neural structure at micro- and macro-scale.
EM imaging principles, file formats, and interpretation.
Understanding segmentation, labels, and sources of error.
Identifying merge/split errors and assessing segmentation quality.
Defining and testing hypotheses using statistical tools.
Exploring cell shape, skeletons, and biofeatures.
Introduction to graphs, adjacency, and connectome structure.
Mapping synaptic connectivity and interpreting motifs.
Storage, querying, and scale-aware design.
Intro to ML concepts and supervised/unsupervised learning.
From filters to deep learning for image understanding.
Using large language models for continuity, errors, and proofing.
Create effective 2D and 3D visuals to communicate connectome data.
Write clear papers, abstracts, and figure captions for neuroscience audiences.
Handling noise, filtering data, and reproducibility.
Creating visualizations to explore and explain findings.
Modeling techniques to interpret neural data.
Ensuring research is findable, accessible, and reproducible.
Communicating ideas clearly with audience awareness.
Sharing work with peers and professionals.
Applying skills and navigating research careers.
Curating evidence of learning and capstone feedback.
Foundations
Knowledge
Skills
Character
Meta-Learning
Motivation
Question
Knowledge
Skills
Character
Meta-Learning
Motivation
Experiment
Knowledge
Skills
Character
Meta-Learning
Motivation
Analysis
Knowledge
Skills
Character
Meta-Learning
Motivation
Dissemination
Knowledge
Skills
Character
Meta-Learning
Motivation