Read widely, deeply and regularly. Read weird things. Keep track of what you read, and make notes; I strongly recommend Zotero with the Zotfile plugin for managing both pdfs and references. Here is a useful overview by Cameron Brick on how to stay on top of the literature.

Some of my favorite papers and books:

Scientific life & organizing your work

• Allen D (2003) Getting things done: the art of stress-free productivity. London: Penguin Books. Staying organized, not overwhelmed.
• Newport C (2016) Deep Work: Rules for Focused Success in a Distracted World. New York. Read the first half with a grain of salt, but see how you can implement the second half’s practices in your own life.
• Gawande A (2010) The checklist manifesto: how to get things right, 1st ed. New York: Metropolitan Books. Must-read for running experiments.
• Schwartz MA (2008) The importance of stupidity in scientific research. Journal of Cell Science 121:1771–1771.

Perceptual decision-making

• Siegel M, Donner TH, Engel AK (2012) Spectral fingerprints of large-scale neuronal interactions. Nature Reviews Neuroscience 13:121–134.
• Summerfield C, de Lange FP (2014) Expectation in perceptual decision making: neural and computational mechanisms. Nat Rev Neurosci 15:745–756.
• O’Connell RG, Kelly SP (2021) Neurophysiology of Human Perceptual Decision-Making. Annual Review of Neuroscience 44:null.
• Carandini M (2024) Sensory choices as logisitic classification. bioRXiv

Statistics

• Aarts E, Verhage M, Veenvliet JV, Dolan CV, van der Sluis S (2014) A solution to dependency: using multilevel analysis to accommodate nested data. Nature Neuroscience 17:491–496.

Behavioral analyses

• Padoa-Schioppa C (2022) Logistic analysis of choice data: A primer. Neuron

Theory and philosophy

• Teller DY (1984) Linking propositions. Vision Res 24:1233–1246.
• Glimcher PW (2005) Indeterminacy in Brain and Behavior. Annual Review of Psychology 56:25–56.

History of science

• Gleick J (1988) Chaos: Making a New Science. Penguin.
• Lindsay G (2021) Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain. Bloomsbury Publishing.

Electrophysiology

• Donoghue T, Haller M, Peterson EJ, Varma P, Sebastian P, Gao R, Noto T, Lara AH, Wallis JD, Knight RT, Shestyuk A, Voytek B (2020) Parameterizing neural power spectra into periodic and aperiodic components. Nature Neuroscience 23:1655–1665.
• Cohen MX (2014) Analyzing Neural Time Series Data: Theory and Practice. MIT Press.
• Humphries M (2021) The spike: an epic journey through the brain in 2.1 seconds. Princeton: University Press.

Learning scientific skills

As a scientists you’ll develop many other skills. Via Mark Humphries, see Dan Larremore’s lab guides to:

• Data visualization
• Giving a talk
• Clean code
• Peer review

A guide for (research) master’s students

Start collaboration

• If you want to learn more about current work at the lab, look into the recent projects here or here
• Reach out to the lab here with a project idea
• Communicate your expectations and goals

  You have an idea, but what then?

  a) Sources for improving programming skills

• Neural Data Science in Python
• Neuromatch Computational Neuroscience Course

b) Read literature

• Check the literature above
• If it is not enough, look for review articles on the topic

c) Communicate with your fellow lab members

• Especially if stuck with code

d) Track your progress