Models of the Mind Review
"When any field of science amasses enough quantitative data, it eventually turns to mathematical modelling to make sense of it." - Grace Lindsay, Models of the Mind, p.312
February 17th, 2023
Models of the Mind by Grace Lindsay promises and delivers a journey through the history of computational neuroscience and "how physics, engineering and mathematics have shaped our understanding of the brain". I read this book for the first time in summer 2022 and recently finished it for a second time as part of a book club with three other neuroscience PhD students in my program and loved it in both contexts. As a solo read it reignited the internal spark of excitement that drew me toward computational neuroscience in the first place and motivated me to continue my own work (a valuable and sometimes elusive feeling for a PhD student). As a group read it was a compelling steppingstone toward fulfilling conversations about the past and future of the field with peers I hope to one day shape that future with. Uniting both experiences was a feeling of being both energized and inspired by a leisurely walk through the history of the field.
Lindsay, now a Professor of Psychology and Data Science at NYU, takes a historical and factual approach to Models of the Mind. She is our guide through decades of fascinating research in neuroscience, physics, mathematics, and computer science. The thread that ties the chapters together is the power of mathematical modeling. Lindsay opens by describing how a Japanese spider, Cyclosa octuberculata, cleverly offloads the burden of remembering where in its web it has previously caught prey by immediately tightening those strands so that they become more sensitive to future prey. In doing so, "the interacting system of the spider and its web is smarter than the spider could hope to be on its own" (8). Much like the spider, she argues, we have no hope of successfully understanding the brain unless we offload some of that complexity. The solution she proposes is mathematical models.
Models of the Mind is organized into a series of chapters, each dedicated to a particular problem in neuroscience that models have been used to address. Lindsay describes models of individual neurons, sensory systems, and the whole brain, giving a wide-ranging tour of the wonderful history and diversity of computational neuroscience. Some of the most cohesive chapters that I enjoyed most were "4: Making and Maintaining Memories", "7: Cracking the Neural Code", and "12: Grand Unified Theories of the Brain". What keeps the book from turning into the dull drone of an introductory course textbook is Lindsay's light tone, illustrative and clear descriptions, and narrative-like style. Each chapter is as much about the models being built as the scientists who built them, and each carefully placed historical tidbit brings the scientists to life so that the book reads more like a story than a listing of historical fact.
What kind of reader will enjoy Models of the Mind? Any scientist working or interested in computational neuroscience will appreciate seeing the field sewn into this far-reaching narrative. The range of topics covered is so broad that there is plenty for any neuroscientist to learn and think about. Undergraduate and early-year graduate students who are interested in computational neuroscience and deciding what kind of research they want to do would benefit immensely from this clear and thorough introduction to the field. No chapter goes into too much detail, but they all drop plenty of suggestions for places to do a deeper dive if interested. Models of the Mind claims to be written with no assumption of prior mathematical knowledge, and Lindsay does a heroic job of explaining all the models without using any mathematical formulas. While a lay reader might take a bit longer than a mathematician or neuroscientist to tackle each chapter, the book is written in an accessible way and would also be a fun read for a friend or family member of a computational neuroscientist who wants to understand more of what they do. All in all, this is the first book I recommend to anyone wanting to learn more about computational neuroscience and an important step toward making this fascinating history of research more accessible to a broader audience.