CPSY 1950 Deep Learning in Brains, Minds & Machines

NeuroAI goals, course structure, and how we will simulate scientific conferences (lightning talks and projects).

📑 Google Slides

No readings or pre-class activities for Week 1—this is the introductory session.

Goal: Put everyone on a leveled playing field for Week 3. Complete the Required Core first (mandatory for all), then the conditional tracks that match your background. You do not need to do every track.

Note on access to papers: For any paper link below, please use the Brown Library proxy links (you may be prompted to log in with Brown credentials off campus).

Required Core — Mandatory for all students

⚠️ Complete both items below before moving on to any conditional tracks. Students with a strong background in cognitive neuroscience may skim, but should still review to ensure familiarity with the course’s framing and terminology.
Expected time: ~90 min total

1. Marr’s Levels of Analysis (video)
   Nancy Kanwisher: “How can we study the human mind and brain? Marr’s levels of analysis”

2. Coarse brain organization (video; focus on gross organization + functions)
   MIT OCW 9.13 (Spring 2019) Lecture 2: “Neuroanatomy”
   Viewing guidance: Watch for the high-level tour of major brain divisions/structures and what they do. Pay particular attention to concepts of receptive fields, neural tuning, and cortical maps — these are fundamental for understanding how neural networks relate to biological systems. You do not need to know fine anatomical details.

Conditional Track A — Cognitive neuroscience foundations

Complete this track if you have not taken at least 1–2 courses in neuroscience or cognitive neuroscience.
Target time: up to ~180 min.

• A1) Neurons, spikes, firing rate (high-level; no ion-channel deep dive)
  BrainFacts: “How neurons communicate”

• A2) Quick map of structures + functions
  NIH/NINDS: “Brain basics — know your brain”

• A3) Methods lectures (videos)
  • MIT OCW 9.13 Lecture 4: “Cognitive neuroscience methods I”
  • MIT OCW 9.13 Lecture 5: “Cognitive neuroscience methods II”

  Note: Don’t worry if the jargon feels overwhelming — you don’t need to memorize all the details right now, and you can always come back to these videos later when these concepts become needed. These lectures include a section on Marr’s levels of analysis; you can skip it if redundant with the required core, though reviewing it twice may be helpful if you haven’t taken cognitive science courses.

• A4) Optional “what measures what?” cheat-sheets (short skims)
  • BrainFacts: “Measuring neural physiology”
  • BrainFacts: “Viewing the human brain through noninvasive tools”

Conditional Track B — Linear algebra intuition (refresher)

Complete this track if vectors/matrices feel rusty.
Target time: ~60 min total.

• 3Blue1Brown: “Vectors, what even are they?”
• 3Blue1Brown: “Linear combinations, span, and basis vectors”
• 3Blue1Brown: “Linear transformations and matrices”
• 3Blue1Brown: “Matrix multiplication as composition”

Conditional Track C — Deep learning intuition (refresher)

Complete this track if backprop/gradient descent are unfamiliar.
Target time: ~60 min total.

• 3Blue1Brown: “What is a neural network?”
• 3Blue1Brown: “Gradient descent”
• 3Blue1Brown: “Backpropagation”

Bootcamp Checkpoint (all students; due Sun 2/2, 2:00pm)

Reading: Crick (1989) — The recent excitement about neural networks (Nature) (Brown Library proxy link)

Reading response due Sun 2/2, 2:00pm: 📝 Submit on Canvas

Optional background papers (only if you want extra framing)

• Cichy & Kaiser (2019) — Trends in Cognitive Sciences (Brown Library proxy link)
• Doerig et al. (2023) — Nature Reviews Neuroscience (Brown Library proxy link)

Architecture, learning objectives, and data shape representations, behavior, and generalization across modalities.

📑 Slides · 📺 Lecture capture

Reading (complete before class): Serre (2019) — Deep Learning: The Good, the Bad, and the Ugly (Annual Review of Vision Science)

Reading response due Tue 2/3, 2:00pm: 📝 Submit on Canvas

Student lightning talks on the three levers of deep learning (architecture, objectives, data).

📑 Lightning talk presentations · 📋 List of lightning talks (Google Sheet)

Reflection due Sun 2/8, 2:00pm: 📝 Submit on Canvas

Pretraining and fine-tuning/transfer; in-context learning and reasoning; what "emergence" claims mean and how to evaluate them critically.

📑 Slides

Reading (complete before class): Firestone (2020) — Performance vs. competence in human–machine comparisons (PNAS)

Reading response due Tue 2/10, 2:00pm: 📝 Submit on Canvas

Student lightning talks on scaling and emerging capabilities.

📋 List of lightning talks (Google Sheet)

No class.

Class on Feb 19 was cancelled (instructor sick).

Background paper (complete before or by Thu):

Serre, T. & Pavlick, E. (2025). From Prediction to Understanding: Will AI Foundation Models Transform Brain Science? Neuron (Brown Library proxy)

Required viewing (watch both):

• Marcus, G. (2024). Keynote at AGI-24. Machine Learning Street Talk. Watch from ~5:00 to ~35:00 (Marcus's talk starts after a brief introduction).
• LeCun, Y. (2024). Objective-Driven AI: Towards AI Systems That Can Learn, Remember, Reason, and Plan. Ding Shum Lecture, Harvard CMSA. Watch the first ~36 minutes. Note: it gets fairly technical in places — if it starts feeling dry, you can stop around the 36-minute mark; you'll have what you need.

Paper response due Sun 2/22, 2:00pm: 📝 Submit on Canvas

Feature visualization, concept-based methods, sparse/dictionary approaches (incl. SAEs); what we can and can't reliably "name" in representations.

Guest lecturer: Dr Thomas Fel (Kempner Institute, Harvard) — thomasfel.fr

📑 Slides · 📺 Lecture capture

Reading (complete before class): Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvinets, A. (2018). The Building Blocks of Interpretability. Distill

Reading response due Tue 2/24, 2:00pm: 📝 Submit on Canvas

Student lightning talks on features, concepts, and sparse methods.

📑 Lightning talk presentations · 📋 List of lightning talks (Google Sheet)

Read: Thorpe, S.J. (1989). Local vs. Distributed Coding. Intellectica, 8, 3–40.

Reflection due Sun 2/29, 2:00pm: 📝 Submit on Canvas

Circuits, causal interventions, and standards of evidence for mechanistic claims.

Guest lecturer: Michael Lepori (CS, Brown) — lepori.xyz

Watch: Jensen Huang & Ilya Sutskever, Discovering the World Model Through LLM (YouTube)

Read: nostalgebraist (2020). Interpreting GPT: The Logit Lens (LessWrong)

Reading response due Tue 3/3, 2:00pm: 📝 Submit on Canvas

Student lightning talks on circuits and causal interventions.

Predicting neural data across measurement modalities; encoding/decoding and representational similarity; what alignment can and cannot justify.

Student lightning talks on model-to-brain mapping.

Treating models as participants in cognitive tasks; behavioral signatures beyond accuracy (generalization, planning, decision making, cognitive control); confounds and best practices.

Student lightning talks on cognitive and behavioral evaluation.

No class

No class

Project launch and evaluation design; in-class time for groups to plan, run pilot tests, and produce first results/figures.

Continue project work: complete runs and draft poster.

Students present project findings in posters (17 posters); structured peer feedback and synthesis discussion.

Students present project findings in posters (17 posters); structured peer feedback and synthesis discussion.

Details TBD

Frontier topics in NeuroAI: global workspace / consciousness & deep learning.

Details TBD

Frontier topics in NeuroAI: generative models, EBMs, cognitive science. Plus course wrap-up and final exam briefing.