Bookshelf

These are some resources that I have greatly enjoyed reading and have helped me better understand my interests, my position in the field, and how I can contribute. The list contains books, research papers, review articles, lessons on academic life, courses, or talks. My idea is to turn it into a sort of bookshelf - rich with pieces from science, art, or people’s experiences.

Science

Papers

Unsupervised identification of the internal states that shape natural behavior
Adam J. Calhoun et al. (Nature Neuroscience, 2019)
A great way to digest this paper and understand the implications of this particular work as well as in general progress on automating animal behavior is this article by Quanta Magazine, To Decode the Brain, Scientists Automate the Study of Behavior from 2019.

Characterizing a psychiatric symptom dimension related to deficits in goal-directed control
Claire Gillan et al. (eLife 2016)

Dynamic sensory cues shape song structure in Drosophila
Philip Coen et al. (Nature 2014)
Check out the accompanying article if you don’t want to get too much into the technical details.

The discovery of structural form
Charles Kemp and Joshua Tenenbaum (PNAS, 2008)
On the ability of humans to construct representations of their environment using only a few observations, transfer those learnings to different environments and leverage them to control their behavior. Follow up with the TEM paper.

Lévy flight random searches in biological phenomena
G.M Viswanathan et al. (Physica A, 2002)
This paper touches upon advantages of Levy walks over brownian motion for random searches and different kinds of Levy walks that originate in the wild depending on how sparse or how mobile the target is. Check out the article Random Search Wired Into Animals May Help Them Hunt by Quanta Magazine from 2020 that summarizes the similar research done across a variety of species since 2002 in an easy-to-understand manner.

Tutorials and Reviews

Introduction to Decision Making models
Paul Cisek (MAIN 2020)

The challenges of lifelong learning in biological and artificial systems
Sashank Pisupati and Yael Niv (Trends in Cognitive Sciences, 2022)
Describes very neatly the problem of continual learning and the framework of contextual or latent-causes proposed to solve it giving a new shape to the problem of learning compared to previous approaches that track a fixed set of parameters. Latent-causes framework is motivated to capture the basis of inductive biases. Further, it talks about few behavioral and neural studies using the framework, and its interesting applications in computational psychiatry.

The what, how, and why of naturalistic behavior
Ann Kennedy (Current Opinion in Neurobiology, 2022)

Resynthesizing behavior through phylogenetic refinement
Paul Cisek (Attention, Perception, & Psychophysics, 2019)
A simpler title would be “Neuroscience needs evolution” but that’s actually another paper. I found the best way to enjoy this paper was to skip the paper and watch this debate instead hosted by Learning Salon.

How do we search for discovery?
Will Dabney (2020)

Academic Life

My Ph.D. advisers expected weekly progress reports. I’m glad they did
Pijar Religia (Science 2021)

Be Skeptical 75%
Nick Cammarata (2020)

Day-to-day process of doing research
Xaq Pitkow (2020)

Richard Gao’s guide to surfing, PhD, (and life) when you suck
Richard Gao (2021)

You can tell how bad you are at something
SMBC Comics (2022)
And a great thread on how to effectively read the papers by Jon Lindsay.

You and Your Research
Richard Hamming (Bell Comm Research, 1986)

Aspects of Scientific Life and Manners
Peter Medawar (1979)

Life

Mental grounding techniques
Crystal Raypole (Healthline 2022)

Empathetic Reappraisal
Nick Cammarata (2019)
“In practice, it’s been most helpful when I first meet someone impressive or untouchably fun and attractive.” It works.

Engineering

Embracing Risk
Marc Alvidrez (Google SRE Book, 2015)