Models of Memory

PSYC 133 @ Dartmouth

Instructor: Dr. Jeremy R. Manning
Time: Thu 2–3:30 & Fri 3–4:30
Location: Moore Library

What’s This Course About?

We explore how memory works by building computational models of:

• Human memory
• Neural networks
• Biological systems

Expect hands-on coding, discussions, and projects!

Goals of the Course

By the end, you'll be able to:

• Build memory models from scratch
• Understand how humans store and recall info
• Critically evaluate computational models

Do I Need Experience?

Required:

• Python

Recommended:

• Stats / Probability
• Bonus: AI, ML, Linear Algebra, Philosophy of Mind

Learning Style

This class is experiential!

• In-class discussions
• Labs in Google Colab
• Problem sets as mini research projects
• Group final project

What We’ll Cover

Week-by-week highlights:

1. Hopfield Networks
2. Memory Search & Recall
3. Context Models
4. Multi-timescale Memory
5. Laplace Transform Models
6. Biological Memory Networks
7. LSTMs & Transformers
8. Final Presentations

Tools We’ll Use

• Google Colab — run and share code
• GitHub — manage your models & projects
• Discord — chat & collaborate

Grading Breakdown

• 4 Problem Sets — 60%
• Final Project — 40%

You can collaborate, but submit your own work
Final projects are done in pairs or small groups

Late & Honor Policies

• 10% per week late (undergrads)
• Follow the Honor Code
• AI use allowed — just cite it

Let’s Build Together!

Come curious, come ready to code, and...

Make memory models awesome!

Reach out anytime: jeremy@dartmouth.edu

Questions?

Check:

• Syllabus (PDF)
• Annotated Outline
• Course site + Discord

Can’t wait to see what you create!