Some fun, interesting, useful and (mostly) free things I have come across on the internet. This is a growing list! Feel free to reach out to me if you would like to suggest any additions to this page.

Computational Cognitive Neuroscience

Tutorials

• Ten Simple Rules for the Computational Modeling of Behavioral Data: a paper on the key steps in building computational models of cognition by Robert Wilson and Anne Collins.
• Neuromatch Academy’s summer course content: syllabus, videos, notebooks and slides from Neuromatch Academy’s Computational Neuroscience summer course.
• Models of Learning: an introduction to computational models of learning in Matlab by Jill O’Reilly and Hanneke den Ouden.
• Computational Psychiatry course: materials from the Computational Psychiatry summer course organized by the Translational Neuromodeling Unit (TNU), University of Zurich, and ETH Zurich.
• Introduction to Reinforcement Learning: a DeepMind and UCL course by David Silver.
• Reinforcement Learning Resource Guide & Tutorial: a resource guide and reinforcement learning tutorial using R by Raphael Geddert.
• Gorilla Academy’s Learning section: a guide to understanding and hosting learning experiments on Gorilla.

Talks

• The Learning Solon: weekly forum exploring bridges and contentions in biological and artificial learning by Ida Momennejad, John Krakauer, and Joshua Vogelstein.
• World Wide Neuro: a comprehensive, live calendar of virtual neuroscience talks and seminars happening around the world.

Podcasts

• Brain Inspired: a podcast about the intersection between neuroscience and AI by Paul Middlebrooks.
• Unsupervised Thinking: a podcast about neuroscience, artificial intelligence, and science more broadly by Grace Lindsay and PhD students/holders in computational neuroscience.

Textbooks

• Computational Cognitive Neuroscience: a free introductory textbook by Randy O’Reilly and colleagues.
• Theoretical Neuroscience: a textbook on computational and mathematical modeling of neural systems by Peter Dayan and Larry Abbott.
• Computational Modeling in Cognition: Principles and Practice: an introduction to the principles of computational and mathematical modeling in psychology and cognitive science by Stephan Lewandowsky and Simon Farrell.

Books

• The Man Who Mistook His Wife for a Hat: the Oliver Sacks book that got me into neuroscience.
• Thinking, Fast and Slow: a book on the two systems that govern human thought by Daniel Kahneman. Links to a Talks at Google video.
• The Book of Why: a book on causality, causal inference, and their relevance in all fields of science by Judea Pearl.
• The Alignment Problem: a book by Brian Christian about how interactions between humans and AI will depend on aligning our goals.
• Natural General Intelligence: a book by Christopher Summerfield that tackles longstanding theoretical questions about the nature of intelligence and how they relate to progress in AI research.

PhD Life and Beyond

• Growing Up in Science: a “global set of conversations about the personal narratives of scientists”.
• Ontology of Value: virtual meetings with PhD who now successfully pursue careers in the industry.
• Roostervane blog: posts about building a career outside academia.

Graduate School

• How Applying to Graduate School Works: a perspective on applying to US cognitive psychology/neuroscience PhD programs by the Sokol-Hessner lab.
• Applying to Psychology Ph.D. Programs: an overview of the journey to US psychology PhD programs, with resources and advice by Kate Nussenbaum.
• How to Pick a Graduate Advisor: a Neuron article by Ben Barres.
• Kisses of Death in the Graduate School Application Process: a survey on common mistakes people make in the graduate school application process by Drew and Karen Appleby.
• How to cold e-mail for a PhD: guidelines on how to cold e-mail professors by Melissa Hart.
• Pursuing Computational Neuroscience: an informative post by Adrienne Fairhall.
• Ten Simple Rules for Aspiring Graduate Students: a prequel to Ten Simple Rules for Graduate Students, with advice to “help you in figuring out whether, where, and how to become a graduate student” by Andrea Luppi and colleagues.