Welcome to the Summer Program in Computational Psychiatry Education (SPICE)! This GitHub repository contains a collection of course materials and resources designed for high school and undergraduate students interested in exploring the field of computational psychiatry and neuroscience.
SPICE is an educational initiative aimed at introducing high school and college students to the intersection of computer science and psychiatry. Students participate in an eight-week program that combines educational tutorials and hands-on research projects within laboratories at the Center for Computational Psychiatry in the Icahn School of Medicine at Mount Sinai.
The Education Track consists of two parallel components in a self-guided, two-week crash course format. Students will complete both to gain a preliminary understanding of computational psychiatry and neuroscience for application to their research projects. Please visit our Jupyter Book to navigate through the course materials: https://center-for-computational-psychiatry.github.io/course_spice/
This component provides students with an introduction to computational psychiatry and neuroscience. Students will participate in lectures, discussions, and hands-on activities. The primary aim of this course is to provide students with a basic understanding of computational psychiatry and neuroscience concepts for application to their research project.
| Computational Psychiatry and Neuroscience | View |
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| Class 1 - Overview of the brain | https://forms.gle/sHWNNTwDUD5DU7QR7 |
| Class 2 - Neurons and neurotransmission | https://forms.gle/ZTMqGXoUSSyUP1uK9 |
| Class 3 - Neuroanatomy and sensory systems | https://forms.gle/LZjMnnLC8tsR8DN3A |
| Class 4 - Neurodevelopment and neural plasticity | https://forms.gle/gbcYKqVPeLuVhx9b6 |
| Class 5 - Neuroscience research methods | https://forms.gle/8RJxgJEkoYnSLecEA |
| Class 6 - Learning, memory, and spatial coding | https://forms.gle/pt5Kt8EdPKRvNs6NA |
| Class 7 - Motivation and reward | https://forms.gle/3ZHPjiqJpNp7cw4T9 |
| Class 8 - Mental disorders | https://forms.gle/kZgwSfsWRTQjoihm8 |
| Class 9 - Computational psychiatry | https://forms.gle/ET99rg45GrzEeCwi6 |
This component provides students with a hands-on introduction to Python programming for data analysis. Over the course of two weeks, students will use Google Colaboratory to learn the fundamentals of Python programming, basic data visualization techniques, and essential statistical analysis methods such as t-tests. The primary aim of this course is to develop practical coding skills and analytical tools to understand data, especially those within the fields of computational psychiatry and neuroscience.
The course begins with an introduction to Python and Jupyter Notebooks. Then, students will then dive into tutorials on Python basics (e.g., variables, data types, conditional statements, loops). As the course progresses, students will learn about data structures, reading and writing dataframes, and utilizing popular libraries such as Pandas, NumPy, SciPy, Matplotlib, and Seaborn for data manipulation and visualization. They will also gain proficiency in descriptive statistics, hypothesis testing, and analyzing relationships between variables. The course culminates with a project in which students will apply these skills to analyze data from their research project.
Please see below for the list of modules within this course.
| Computational Science | Run | View |
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| Module 00 - Introduction | |
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| Module 01 - Jupyter Notebooks |  |
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| Module 02 - Python Basics | |
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| Module 03 - Data Structures | |
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| Module 04 - Data Processing | |
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| Module 05 - Data Visualization | |
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| Module 06 - Numerical Operations | |
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| Module 07 - Hypothesis Testing | |
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| Module 08 - Linear Modeling | |
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| Module 09 - Final Projects | |
This track matches students with a mentor for the summer based on research interests and availability. Students will work on a research project under the guidance of their mentor and present their findings at the end of the program. The primary aim of this course is to provide students with hands-on research experience in computational psychiatry and neuroscience. Please coordinate with your mentors to determine the scope and timeline of your project.
We welcome contributions from the community. If you have additional suggestions or corrections, please submit a pull request. Together, we can create an educational resource for future participants. Please follow these steps to contribute:
- Fork the repository to your GitHub account.
- Make the desired changes or additions in your forked repository.
- Submit a pull request, detailing the changes you made and the reasons behind them.
Our team will review your contribution and merge if it aligns with the goals and scope of the repository.
If you have any questions, suggestions, or feedback regarding the Summer Program in Computational Psychiatry Education (SPICE) or this repository, please feel free to open an issue in the repository or contact us directly. We are here to help you and make your learning experience as enriching as possible.
 Shawn A Rhoads |
 Sarah M Banker |
 Alisa M Loosen |
Content in this book (i.e., any .md or .ipynb files in the content/ folder) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
