Rating: 3/5
Surprisingly good for a free textbook. This book is recommended for students competing in the Brain Bee competition and it’s decent. The level of detail is below an undergraduate textbook though and is biased towards the medical side of neuroscience. For instance, the book covers more disorders and abnormalities than usual. However, it’s a great introduction or review of the basics of the brain.
Rating: 3/5
Your average literature-review-style textbook. Some of the chapters are great, some aren’t. It felt like some of the chapters tried to force their findings into the framework of deliberate practice but it isn’t genuine. The chapters by Ericsson are great but I would recommend to read his book “Peak” instead.
Rating: 4/5
A very good book on how we see. Although a bit dated and reads like a literature review at points, the ideas are excellent and well connected. The theories make sense and are supported by evidence that you can try for yourself in the figures. I liked how the ideas were connected into a general framework while different aspects of vision were deconstructed. But I didn’t like the writing at points and the textbook could be more concise.
Rating: 1/5
I struggled to get through this textbook. While the first part was ok and it introduced Marr’s three levels, the rest of the book feels dated and too technical. The heavy reliance on stereograms means that if you can’t see them (like me), you can’t do most of the examples. And stereograms aren’t representative of naturalistic stimuli so the arguments based on them are weaker. Another weakness was the lack of link to action/behavior and the lack of neurobiological detail. Sure, we can come up with technical theories of how vision works, but without verifying them against evidence, they’re just hypotheses. And that was the main feeling I got from this book: just a collection of outdated vision hypotheses. I wouldn’t recommend this textbook to learn anything about vision as our current understanding has vastly changed in the last 40 years. Instead, pick up “Principles of Neural Science” for the neuroscience of vision or “Principles of Neural Design” for the computatio behind vision.
Rating: 3/5
I’ve been wondering why the brain is described as a “small-world” network and this textbook explains what this phrase means (high clustering with short path lengths). Written by one of the important figures in network neuroscience, Olaf Sporn, it’s an ok textbook. The application of network science to the brain is fascinating and there’s potential for it to answer our questions about the brain. However, this textbook only has surface-level answers and the writing was dry. Like many other textbooks, this one was mostly a literature review with some good ideas thrown in here and there. The ideas are interesting but the presentation is lacking. Specifically, one of the biggest weaknesses was the lack of connecting brain activity to behavior, as brain dynamics don’t mean anything in themselves.
Rating: 5/5
The bible of neuroscience and a tome at over 1000 pages, this textbook is an encyclopedia of neuroscience knowledge. Both depth and breadth are covered but it was lacking in all areas such as the vision chapters that skipped over the LGN and color processing. The textbook also has no mention of any computational models such as the Hodgkin-Huxley equations of the action potential and has a biological bias towards genes and proteins. While this is expected, don’t expect this book to cover all of our knowledge on the brain as it’s missing a lot of psychology and computational viewpoints. But what it does cover, it does well as the writing and illustrations are excellent. Overall, this is a half-step from the introductory neuroscience textbooks. It doesn’t reach the level of papers or specialized textbooks, but it’s a good bridge from beginner to intermediate knowledge.
Rating: 3/5
Once again, a “see how many authors we can fit into a textbook” textbook but done ok-ish. Some of the chapters get very technical and lose sight of the bigger picture, some don’t. I liked how each chapter is posed as a question and the answer is never simple, but the lack of a coherent story or theme to the papers is what drives the low rating. Also, some of the ideas aren’t new or are questionable based on fundamental neuroscience knowledge. Overall, an ok read.
Rating: 5/5
I’ve been focusing a lot on neuroscience so I wanted to learn more about psychology, it’s older sister. This is an intro/applied psychology textbook and I loved it. There’s almost no mention of neuroscience and it mostly talks about the mind in terms of daily life. The ideas here are almost never covered in neuroscience textbooks, topics like stress, sex, marriage, careers, sexuality, and social life. These topics are related to the brain but the approach is very far from neuroscience. This textbook is, in some ways, like a self-help book but based heavily in science and I really liked the writing style. The textbook would introduce some results but also relate them back to the general picture and how it’s applicable to life. Overall, I loved this textbook.
Rating: 1/5
I got into this textbook because the introduction hooked me but it fell apart from there. I was expecting some more principles of the nervous system and the research approach outlined in the intro was promising but undelivered. The ideas are outdated and we’ve progressed a lot since this textbook was written (2002). This textbook is another one of those cases where the author pushes for their own theory of the brain but unlike some of the other textbooks I’ve read like this, this textbook failed to be interesting and doesn’t answer the questions I have about the brain.
Rating: 3/5
I read this textbook to learn more about information/communication theory and the intuitive side of it, and it delivers. The last few chapters weren’t interesting to me since the book is old, but the core of information theory is well taught here. I didn’t like the emphasis on connecting language to information theory, but that’s just me.
Rating: 3/5
Another one of those “collection of papers equals textbook” textbook but the overall quality of the papers here is good. I enjoyed the beginning and end parts of the book, not so much the middle due to disinterest. I really liked the last chapter as it introduced a new consciousness idea to me and it gave a more overall picture of consciousness. When people say that we don’t know much about consciousness, they should read this textbook because there’s so much that we do know and it’s taught here. It covers the three main areas of consciousness: the self, the contents, and the state.
Rating: 1/5
I dropped this textbook due to the overall poor quality of the content and presentation. The writing was awkward, bad, or had mistakes, and the images were like made on MS Paint. The introduction on AER is good because I’ve encountered that idea in neuromorphic papers, but the rest of the textbook is garbage. There’s no coherent theme or story and it’s just a mishmash of ideas. There’s no discussion on the overall principles of neuromorphic computing and focuses more on the engineering side of the field. Mainly, the implementation and circuitry of neuromorphic computing. Overall, wouldn’t recommend, just go read neuromorphic papers instead.
Rating: 2/5
This is one of those “slap a bunch of papers together and call it a textbook” textbook and some chapters are great, some aren’t. You can tell which chapters interested me by how many notes I wrote for it, but overall I just didn’t find many chapters interesting. Some chapters dive deep into some specific species that I don’t care about and doesn’t link it to our understand of the evolution of the brain. I hate those kinds of “here are the facts, how they fit into the bigger picture we don’t know” chapters, which there are a few of here. This is the issue with these types of textbooks, a collection of papers doesn’t make for a good story nor does it link separate ideas into a coherent theory or understanding.
Rating: 4/5
The bible for cognitive neuroscience written by the field’s founder himself, this textbook is great for its level of detail and emphasis on cognition. However, I found a lot of the material to just be review of an introductory neuroscience textbook and it was sometimes just a regurgitation of the results from some uninteresting paper. If you skip the review parts like I did, then the textbook becomes more enjoyable as it delves into more details than an intro textbook, especially the experiments into cognition like split-brain patients. Overall, a decent textbook.
Rating: 2/5
I went into this textbook being (somewhat) open minded to the philosophical side of neuroscience. I’ve been avoiding the philosophical side of neuroscience because I just find their ideas unjustified and neglecting the evidence we have of the brain. Like the mind-body problem shouldn’t be a problem and unconscious zombies have never been shown to exist. Avoiding that topic, this textbook didn’t really change my mind about the philosophy of the brain and mind. I did enjoy how the author mentioned neuroscience evidence such as the history behind the discovery of long-term potentiation, but I didn’t enjoy the writing. Philosophical writing comes off to me as pretentious and overly complicated for no good reason. I think the ideas in this book are interesting but not their presentation.
Rating: 4/5
I read this textbook hoping to find a more theoretical approach to neuroscience and found some of it here. What this textbook discusses, and what I want to learn, are the principles behind the nervous system and brain. Why are some things the way they are? What are they useful for? I found some of the answers here and this textbook is special in that regard. I really liked the clear exploration and reasoning behind each principle, but the writing was difficult to read throughout the book. Sometimes, I would get mad at the authors for having said a simple idea in a complex and confusing way. I also disliked the passive style of writing found throughout the textbook but I tolerated it because the ideas were interesting. I really want to give this textbook the highest rating but the writing is very bad.
Rating: 5/5
I read this textbook to learn more about neuroanatomy and it definitely delivers. It’s very large at 1000 pages but the amount of detail covered is excellent. The approach to learning neuroanatomy through clinical cases is great because it links structure to function. So if you only want to learn about the structure of the nervous system, I would skip this textbook. There’s also a neurology spin to the knowledge since clinical cases are used which I found interesting and insightful. I’ve mostly been approaching neuroscience as a science but this textbook showed me that there’s also the application side of neuroscience, the medical side that actually helps people. Overall, an excellent textbook and a great reference.
Rating: 4/5
My introduction to the field of computational neuroscience and this textbook was good. I liked how the author slowly built up the complexity of modeling from neurons to networks to systems. But I didn’t like the heavy emphasis on models and the lack of integration between the different levels of nervous system organization. Only now do I realize that computational neuroscience is a field of building more accurate, faster, efficient models, but I thought it would’ve emphasized the coding and computational properties of neurons more. An analogy would be like thinking computational physics as computing with physics (E.g. Quantum computers) but it’s actually modelling physics using a computer. I thought this textbook would focus on how neurons compute and how they connect to form pathways, circuits, networks, and systems, but this isn’t the case. Overall, a good textbook, but not one I’m especially interested in.
Rating: 3/5
I learned some new knowledge about how cognition develops using this textbook, which is why I give it an average rating. I liked how the theme throughout the book was that there are three main theories about how cognition develops, but the one that evidence supports the most is the interactive specialization theory. However, I do take some points away for the ‘paperish’ style of writing the textbook has at times. By this, I mean that the textbook sometimes states results without linking it back to the bigger picture, or it gets too technical at times which makes it difficult to read.
Rating: 3/5
A good introduction to JavaScript if you already know how to program. Otherwise, there are other, and I think better, intro to programming textbook out there. I don’t think a new programmer should start with JavaScript due to the many exceptions and weirdness it has, but overall, this is a good JS textbook.
Rating: 3/5
This book describes the Neural Engineering Framework (NEF) by Chris Eliasmith’s group at the University of Waterloo and it’s applications. Overall, I found this textbook a bit too philosophical for my tastes and the writing was a bit hard to read at times. However, the technical details of the NEF are great and seem to be closely related to biological brains. I really liked how the author always related back the results of NEF models to actual neuroscience/psychology experiments and appreciated the validation against actual biological data. Not all “explaining the brain” frameworks or theories relate back to actual brain data (looking at you ARC) so it’s much appreciated here. I don’t agree with all of the points NEF makes, but I do understand them.
Rating: 5/5
From the creator of Keras, a popular DL library, comes this amazing textbook. This was my first introduction to practical/programmable machine and deep learning, and it couldn’t have been better. Previously, I would just read random articles and watch random YouTube videos on ML/DL but never programmed a DL model. With this textbook, I could write an ML program and know fundamental ML principles. However, one drawback of this textbook is that it provides you the principles without showing you how they became principles. This is both good, because it avoids the heavy math behind DL, but also bad because then you lack the depth behind those principles. For a more details on the math behind DL, I recommend “Deep Learning” by Ian Goodfellow. Overall, this is a very practical introduction to DL and I would highly recommend it to anyone interested in programming DL models quickly.
Rating: 4/5
I really like this textbook for it’s fun writing and interesting ideas. The author runs with the theme of analog vs digital and explores whether the brain is analog or digital (spoilers, it’s both). In the second half of the book, the author pushes their own theory of how the brain does computation (with waves) which I still think is important but not talked about much in research. The idea is a obscure but the principles behind it are solid. Overall, a great textbook.
Rating: 3/5
While I did drop this textbook, I don’t think it’s bad, it just didn’t fit my interests. This textbook is well known to be one of the best textbooks in AI and for good reason. It covers a large variety of topics, some basic to programming (like searching, BFS, DFS, first-order logic), some new (ML, RL, Bayesian models, NLP). Don’t put too much weight on my opinion since I didn’t finish it, but it’s a good textbook, just not good for me.
Rating: 5/5
The first cognitive science textbook I read and one of the best textbooks I’ve read. It’s well organized and the ideas introduced were new and exciting. It also linked together the different fields of cognitive science into a coherent story that was exciting to read and follow. The textbook covers a bit of everything from computer science, neuroscience, psychology, mathematics, and philosophy. The visuals were great and the writing strong. Overall, I would highly recommend this to anyone interested in AI, the brain, and the mind.
Rating: 3/5
This textbook starts off strong but gets weaker the further you get into it. I remember being really disinterested in the later chapters, such as chapters 18 to 20, because the author would just talk about random acronym projects that don’t matter. However, I didn’t give this textbook a completely bad rating or dropped it because the start is very relevant to the history of AI (Alan Turing, Dartmouth meeting), and I did encounter some of the acronym projects in future textbooks such as ARC, GOFAI, and PDP. Overall, an ok AI history textbook, just skip the parts that don’t interest you.
Rating: 5/5
This was the first neuroscience textbook I read and it’s really good. There are three well known introductory neuroscience textbooks: this one, Neuroscience by Dale Purves, and From Neuron to Brain by John Nicholls. I’ve only read this one because it’s just that good; you don’t need to read any other one. I really enjoyed the writing style in that it’s a bit informal and fun. The only issue I had was that it lacked details in everything it mentioned. I mean, the textbook is already 1000 pages long (which is very long for a textbook) and it’s an introduction textbook, but the lack of detail made me search for the answers somewhere else. If you’re looking for the details or the next step, I recommend “Principles of Neural Science” by Eric Kandel, which is a graduate-level encyclopedia of neuroscience knowledge. Otherwise, this textbook is a great introduction to the science of the brain.