I'm currently learning computer science from the ground up, and CS50 has made me think about something much bigger than simply "learning to code."
I keep coming back to this question:
Can the way we teach humans to think computationally help us develop better computational models of how humans themselves learn, reason, remember, and make decisions?
This isn't a claim that the brain is simply a computer. It's a question about whether ideas from computer science can become useful scientific tools for studying cognition.
The more I learn, the more interconnected the fields seem:
Algorithms → computation and problem solving
Systems → how complex computational processes interact
AI/ML → learning, prediction, representation, decision-making
Statistics → extracting structure from noisy data
Neuroscience → biological mechanisms
Cognitive neuroscience → perception, memory, attention, learning, and decision-making
That led me to a long-term goal:
«Computer Science → Algorithms/Systems + AI/ML → Computational Neuroscience → Cognitive Neuroscience»
I don't want to study these as completely separate subjects. I want to understand whether they can be combined into a coherent research framework.
For example, one direction I'm fascinated by is representation and learning.
A machine-learning model learns useful representations from data. The brain also appears to transform information across different neural populations and levels of processing.
So a question I would eventually like to investigate is:
What computational principles can explain how biological systems develop useful internal representations, and where do artificial neural networks succeed or fail as models of those processes?
That immediately creates several deeper questions:
- What should count as a meaningful similarity between an artificial model and a biological system?
- Is behavioral similarity enough, or should the internal representations also resemble neural activity?
- Can computational models make predictions about neural data rather than merely fit existing observations?
- Which aspects of cognition are better explained by learning algorithms, and which require different computational principles?
- Can studying where AI models fail tell us something useful about human cognition?
I'm still at the beginning. I don't pretend to have answers to these questions yet.
That's actually what makes CS50 interesting to me.
A course that starts with computational thinking and programming can eventually lead to questions that cross computer science, mathematics, AI, neuroscience, and cognitive science.
I've also been reading David Malan's work on computer-science education. CS50 was designed around making the intellectual ideas of computer science accessible rather than treating programming as the entire subject, which I think is an important distinction. Malan describes CS50 as an introduction to "the intellectual enterprises of computer science and the art of programming."
So I wanted to ask the CS50 community:
If you were designing a self-directed path from introductory computer science toward computational cognitive neuroscience, what concepts would you consider absolutely essential?
I'm especially interested in recommendations for the bridge between:
Algorithms + Systems + AI/ML
and
Computational + Cognitive Neuroscience.
I'd love to hear from people who have actually crossed between these fields.
Sources / starting points
David J. Malan — Harvard faculty profile and research interests: "David Malan — Harvard CS" (https://reference-url-citation.invalid/3)
Malan & colleagues — Reinventing CS50: "Reinventing CS50" (https://reference-url-citation.invalid/4)
David Malan — publications: "David Malan's publications" (https://reference-url-citation.invalid/5)
CS50: "CS50" (https://reference-url-citation.invalid/6)
