deploy: 872f03e

Author: jeremymanning

README.html

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 </ul>
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Slides</span></p>
 <ul class="nav bd-sidenav">
-<li class="toctree-l1 has-children"><a class="reference internal" href="slides/links.html">Downloadable slides</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="slides/welcome_wrapper.html">Welcome and Overview</a></li>
-</ul>
-</details></li>
+<li class="toctree-l1"><a class="reference internal" href="slides/week_1.html">Week 1</a></li>
 </ul>
 <p aria-level="2" class="caption" role="heading"><span class="caption-text">Assignments</span></p>
 <ul class="nav bd-sidenav">

_downloads/1b0e9d32b144d021a4b8e21319233db3/how_to_read.html

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+---
+marp: true
+theme: default
+class: invert
+math: katex
+author: Jeremy R. Manning
+---
+
+# 📚 How to Read a Paper  
+## (When You're Building a Model)
+
+---
+
+## What’s the goal?
+
+- Extract what’s **important**
+- Figure out how to **replicate core results**
+- Build something that **actually works**
+
+---
+
+## Step 1: Skim first 🔍
+
+- Read the paper **once**, high-level only  
+- Focus on:  
+  - **Abstract**  
+  - **Intro**  
+  - **Discussion**
+
+🧠 Skip hard parts — it's okay!
+
+---
+
+## Step 2: Zoom in 🔬
+
+Re-read the paper:
+
+- Focus on the **Results**
+- Find the **core result(s)** you’ll replicate
+
+🧪 Reproducing a figure is often enough!
+
+---
+
+## Step 3: Deep dive 🧠
+
+Go **sentence by sentence** through Results + Methods  
+Highlight *anything* needed to reproduce results:
+
+- Equations 🧮  
+- Algorithms ⚙️  
+- Diagrams 🧭  
+- Code snippets 💻  
+- Implementation notes 🧱
+
+---
+
+## Understand everything
+
+For each highlighted thing:
+
+- Use AI (carefully!) 🧑‍💻  
+- Watch a video / read a blog 📺  
+- Try running shared code 🔁  
+- Ask someone 🗣️  
+- Take **lots of notes** 📝
+
+---
+
+## Step 4: Make a task list ✅
+
+- From your notes, write out a **to-do list**
+- Start high-level → break into sub-tasks
+- Track your progress (Google Doc, GitHub Projects, etc.)
+
+💡 Tip: Write it out by hand if it helps you think
+
+---
+
+## Missing a detail?
+
+Try this:
+
+- Re-read carefully (look for footnotes!)  
+- Check **supplemental materials**  
+- Look at **shared code**  
+- Email the authors! 📬
+
+---
+
+## How to email an author 💌
+
+- Be **clear and concise**  
+- Ask **all your questions up front**  
+- Be respectful of their time  
+- Say thank you 🙏
+
+---
+
+## Be kind to yourself ❤️
+
+This is hard!
+
+- Take breaks 🧘  
+- Talk it out (even to yourself!) 🗯️  
+- Go for a walk 🚶  
+- Sleep on it 😴  
+- Keep chipping away 🔨
+
+---
+
+## Final tips 🧠
+
+- Building from primary sources is *real science*  
+- Confusion is part of the process  
+- Ask questions, keep notes, keep going!
+
+You got this 💪

_downloads/690fce98b26bc1d590777fe332e13079/intro_to_models.html

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+---
+marp: true
+theme: default
+class: invert
+math: katex
+author: Jeremy R. Manning
+---
+
+# 🧠 What is a Memory Model?
+
+---
+
+## Models of memory = 🤖 for the mind?
+
+A **memory model** is like a *little machine* that:
+
+- Takes in **sequences** of inputs  
+- Stores **representations**  
+- Produces **memory behaviors**
+
+🎯 It lets us *simulate* what minds (and brains) do!
+
+---
+
+## What's the *input* to a model? 🧩
+
+Usually:
+
+- A **list or sequence** of experiences  
+- Could be:  
+  - 📝 Words  
+  - 🧠 Concepts  
+  - 🌍 Sensory events  
+  - 🕰️ Time-varying contexts
+
+---
+
+## What's the *output* from a model? 🎬
+
+Behaviors we can measure:
+
+- 🗣️ **Free recall**, **recognition**, etc.  
+- ⌛ **Timing**, **errors**, **response curves**  
+- 🔄 How memory *changes* with new input
+
+---
+
+## 🤔 What kinds of models will we see?
+
+From the [course outline](https://contextlab.github.io/memory-models-course/outline.html):
+
+- 🧠 **Hopfield nets** (attractor memory)
+- 🎯 **Search & recall** processes
+- 🧩 **Contextual encoding** (e.g. retrieved context model)
+- 🧪 **Laplace-transform–based** systems
+- 🧬 **Biological circuits** for memory
+- 🤖 **Modern deep nets** (LSTMs, Transformers)
+
+📚 We’ll build, analyze, and compare them!
+
+---
+
+## 🔍 How do we evaluate models?
+
+- ❓ **Does it fit the data?** (qualitatively or quantitatively?)
+- 🧪 **Does it predict new behaviors?**
+- 🧠 **Does it teach us something about cognition or the brain?**
+
+---
+
+## 🧱 All models are approximations
+
+> “All models are wrong, but some are useful.”  
+> — George E. P. Box (1976)
+
+We’re not trying to recreate a brain; we’re building **simplified systems** to *understand memory better*.
+
+---
+
+## 🎯 Goals when using models
+
+- Break down **complex behavior** into understandable pieces  
+- Generate **testable predictions**  
+- Build bridges between **psychology**, **neuroscience**, and **machine learning**
+
+🛠️ Memory models are tools — let’s learn how to use them.

_downloads/b2d84e6aef1cf7438c3360dacd360b14/welcome.html

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+# Week 1
+
+### April 3, 2025:
+  - [Welcome and Overview](welcome.html)
+  - [Introduction to Models](intro_to_models.html)
+  - [How to Read](how_to_read.html)

_sources/slides/how_to_read.md

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 ---
 marp: true
-theme: cdl-theme
-paginate: true
+theme: default
 class: invert
+math: katex
+author: Jeremy R. Manning
 ---
 
 # 🧠 Models of Memory  
@@ -17,9 +18,10 @@ class: invert
 # What’s This Course About? 🤔
 
 We explore **how memory works** by building **computational models** of:
-  - Human memory  
-  - Neural networks  
-  - Biological systems  
+
+- Human memory  
+- Neural networks  
+- Biological systems  
 
 🧪 Expect **hands-on coding**, **discussions**, and **projects**!