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Student starter pack

If you're a student newly starting in the lab, this page is made for you. It is packed with resources and knowledge that you will find useful during your time with us. Enjoy!

The starter pack is divided into three sections. In no order of importance, you will find important papers from authors we might refer to every once in a while, coding tutorials to get up to speed with Python and develop your programming skills, and miscellaneous resources that don't fit in these two categories but are still relevant.

Coding tutorials

Developing strong coding skills is crucial for success in our lab. Here are some excellent resources to help you improve your Python programming and data science skills:

  1. ⭐ The Good Research Code Handbook: This is a must-read for anyone joining the lab. It covers essential knowledge on how to structure and write your code in research, suitable for both beginners and experienced researchers.

  2. You can find some fun and interactive Python tutorials on DataCamp and Codecademy.

  3. Software Carpentry Python Fundamentals: Basic Python concepts for beginners.

  4. EdX: Using Python for Research: Free course on Python applications in research.

  5. Scientific Python Lectures: Advanced course for confident programmers.

  6. Python Data Science Handbook: Comprehensive guide to data science with Python.

  7. If you know nothing about it, take some time to learn the Unix Shell and the essentials of Git & GitHub.

  8. If you're going to use MatLab, you can check this live script tutorial to learn the fundamentals.

Miscellaneous

  • Academic Writing

    Learning how to write is fundamental to academic training. If you're struggling with writing or structuring your papers, check out:

    Ten Simple Rules for Structuring Papers

  • Machine Learning Fundamentals

    To understand the basics of machine learning and modeling, this Coursera class is a must:

    Machine Learning Specialization

  • Awesome PhD Resources

    A curated list of carefully selected tools and resources for both early career and senior researchers:

    Awesome PhD Repository

  • Awesome Neuroscience Resources

    Curated list of awesome neuroscience libraries, software and any content related to the domain.

    Awesome Neuroscience Repository

Papers & lectures

This section contains some foundational papers to give you some background on the research going on in the lab, as well as interesting lectures if you can't find anything to watch on Netflix. This list might not always be up to date, but you can find our latest publications on the lab website.

Bracci & Op de Beeck 2023

Grill-Spector and Weiner 2014

DiCarlo et al 2012

Kravitz et al 2013

Op de Beeck et al 2019

  • The classic publication by Felleman & Van Essen 1991 covers the intricate pattern of connectivity that characterises the ventral stream and the visual system in general.

  • This publication by Roger Shepard 1980 is a pioneer in the development of multivariate analyses.

  • The study by Bracci et al. 2016 is a good illustration of how we use carefully crafted stimulus sets in combination with multivariate fMRI to answer questions about visual representations in the brain.

  • More recent examples in the lab include Ritchie et al. 2021 and Yargholi & Op de Beeck 2023.

  • The foundational paper by Kriegeskorte et al 2008 introduced the use of representational similarity analysis (RSA) to compare representations across brains, species, models and more.

  • If you feel like DNNs and brains are a great match, check out this reading list by Anna Wolff and Martin Hebart.

  • The fundamental papers of Yamins et al and Khaligh-Razavi & Kriegeskorte both published in 2014 showed for the first time that DNNs develop similar representations to the brain.

  • In our lab, Kubilius et al 2016 showed that the representations of shape in DNNs display some properties that had been seen before in the human brain and perception, and not in earlier artificial models.

  • Among the very often citer papers is the publication by Geirhos et al 2022 which showed that DNNs are biased towards texture, suggesting that their processing of shape is still not the same as in humans.

  • Opinions diverge on how to use modelling to further our understanding of the brain. This 2023 BBS paper by Bowers et al surely started a serious discussion on that topic.

  • The tradition in the lab to use carefully crafted stimulus set is a promising approach to understand the differences between human visual processing and DNNs, as for example illustrated by Bracci et al 2019.