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I didn't find Theoretical Neuroscience particularly readable as others in the thread have said, but it is the go-to book for the classic topics in the field. I found Fundamentals of Computational Neuroscience to be a much much better book for introductions. From Computer to Brain : Foundations of Computational Neuroscience was fairly approachable. On the more cognitive side, From Neuron to Cognition via Computational Neuroscience was pretty good. If you like the nonlinear systems side, Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting was pretty tough to read but full of good content.

It really depends on what subsets of comp neuro you're most interested in. I worked mostly on the cognitive side, and I was never super satisfied with any books on comp neuro in that area. I think the field is just too young for a great summary to exist beyond the neuronal/small network level.

There is a ton of interesting mathematics that goes into other areas of neuro that wouldn't typically be included in Computational Neuroscience. Different imaging methods, for instance, have some pretty fun math involved and are very active areas of research.

I was in the exact same position as you Junior year and I went on to a small liberal arts college that didn't offer an undergraduate degree in neuroscience but did have some classes in the field. I also plan on working for a few years after graduation to get more experience in the field since my university did not offer it. Neuroscience is a relatively new field and hasn't grown enough yet to become its own department at most universities but rather, as was the case at my university, an interdisciplinary focus. If you are certain that you want to do neuroscience (which admittedy is a lot to ask since you haven't come up against classes like Organic Chemistry) than you should maximize your exposure to the field despite the fact that your future university may have a neuroscience program that is anywhere between its own department and non-existent.

For me this meant taking both dedicated neuroscience classes at my college but also doing research with the only professor doing neuroscience research for two years. I also do a lot of learning on my own working through neuroscience texts; a good book that comes to mind is Principles of Neural Science. I echo the opinion of /u/radicalpi in that the program varies widely between universities in terms of what classes it requires: some will have a greater focus on psychology (Cognitive Psych) while others will focus more on the biology and chemistry. I also agree with his/her opinion that you might be better served majoring in biology or chemistry if that component of neuroscience interests you more. I majored in Biochemistry and Math and had my university offered something along the lines of a Cognitive Sci major, I would not have majored in it since I am more interested in the "bottom-up" perspective. One last comment: if math or physics at all interest you, I would suggest looking into mathematical neuroscience or related subfields. In the neuroscience program at my school, most of the students that took neuroscience courses with me were psych majors and I think this is true of many universities. The problem with this is that to understand developing concepts such as neuronal dynamics and to understand technical advances in the field Hodgkin-Huxley/Fitzhugh-Nagumo, fMRI, and optogenetics requires a good grasp and comfortability with math and physics that is inaccessible to a lot of people in the field. This can only serve to help you break into neuroscience in the future.

I second this comment; Hobson is one of the biggest names in the field of dream research and has released a lot of plausible hypotheses and working models. I don't have much time atm (gotta go to the library and study some more Cognitive Neurosciences coincidentally...), but here's a comment of mine regarding dream sleep on an older question.
Take what I wrote not too precisely, I was and am still young in this field (for example I misinterpreted the article slightly. REM activity is essential and characteristic for dreaming but the article doesn't exclude the importance of other possible activity regarding dreaming). Just take the general gist and check out the articles and the ones they cite in it.

To answer your question more specifically, I highly recommend getting familiar with Cognitive Neurosciences in general.

A first step would be a good textbook. The following is very up to date, easy to read, and well illustrated:

• Cognitive Neuroscience: The Biology of the Mind (Fourth Edition) - Gazzaniga, Ivry, Mangun (2014)
  
  For online self study including lectures and exercises I highly recommend a good MOOC like this Edx course from Harvard.
  
  If dreaming is your interest, some things to focus on is consciousness, memory, relation between function/anatomy, neuroimaging techniques, and general sleep physiology.
  
  In general, neuroscience requires (or at least highly benefits) understanding the brain as a (biological) computational system. It's not to be understood as simply as a single organ. It is a highly complex system capable of an extremely broad range of functions. Dreaming is just one of the many (but very important) functions it can do.
  
  If I find some time I'll try to add some more information. I'm no expert on Cognitive Neurosciences but if you have any questions feel free to ask! Also don't hesitate to ask around on /r/askscience, or check out threads regarding neurosciences. There are some really smart people roaming around those threads.

I am super interested with no background too! I read Neuroscience For Dummies on my kindle. The format was a little wonky, so I recommend getting the paperback. It was interesting and a semi-easy read. I went to Star Talk with Neil deGrasse Tyson and the topic was The Science of the Mind. It was great! I chatted with Cara Santa Maria and asked about her recommendations for interesting neuroscience books. She said I'd love The Man Who Mistook His Wife For A Hat. I've been meaning to read it! Also, checkout Amazon's best sellers in Neuroscience. Read reviews and see if they fit your interest. Let me know if you find anything interesting.

Currently reading a book titled Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts, I would highly recommend it if you're interested in the science of consciousness.

In terms of going back to uni to do an undergraduate in neuroscience, i don't think it's worth it. I'm about to start an honours in visual neuroscience, but before finding my project I was talking to a prof about honours projects and he said he had a computer science graduate doing a project with him on alzheimer's. A lot of neuroscience these days involves programming so you have a huge one up there (i'll be learning programming for my project). In terms of how to get into the field, you could probably go straight into post grad if you have good marks with your current undergrad degree. Honours or Masters degrees, or as ciaoshescu mentioned you may be able to do an internship, i'm not to sure how that would work though.

Good luck on your journey!

Hello past me! I got my BS in biomedical engineering and now I'm in my first year of a neuro phd program, woo!

Definitely brush up on the basics, maybe borrow an intro neuro textbook from a library (I skimmed through From Neuron to Brain before I started). Yes, you will be taking some "intro" courses the first year, but most of my professors teach the class with the assumption that the students took neuro classes in undergrad, which I did not (plus I graduated from undergrad in 2009...).

If you know what you're interested in and could post it in here, we might be able to come up with some interesting papers or good books to read that are more specific to you. For example, I am interesting in cortical networks and my PI suggested I check out Connectome. I will be honest, I have not read it yet as I have plenty of papers I have to read every week, but I plan on getting to it over the summer.

Going to jump in and take a stab at responding, if nobody minds...

Neuropsychology uses mathematics very rarely. Neuropsych is more about brain injury and rehabilitating the person around whatever neural issue they have. Neuropsychologists typically operate as part of a clinical treatment team, working alongside a neurologist, maybe a neurosurgeon if there was some intracranial or CNS trauma involved, and some sort of physical, behavioral, or cognitive therapist. In this team, neuropsychologists usually run the tests to diagnose disabilities or track symptoms over time. If you're interested in the neuroscience of psychology/cognition, you may be more interested in cognitive or behavioral neuroscience. These fields rely on mathematics but in a different way because the observations at that level are so hard to quantify. Mathematics in cognitive neuroscience (and even neuropsychology) is more about measurement theory: quantifying abstract or immeasurable phenomena and then attempting to explain how high-level function is tied to low-level events. Stuff that comes to mind includes the neurobiology of autism, visual attention, information processing in sensory networks, etc. This will lead into Bayesian decision theory, information theory, psychophysics, probability models, and from a very theoretical side, graph theory and looking at the mathematics of network topology and multi-sensory integration.

Mathematics is used in neurochemistry (or, more precisely, in fields like biochemistry, neuroendocrinology, neuropharmacology, biophysics, etc). In those fields, math is often used to describe the dynamics of whatever system you are studying, whether it's some kinetic process like diffusion or changes in protein conformation or receptor/chemical binding dynamics or even chemical metabolism. For this, you'll really want to know your differential equations and dynamical systems. The Dayan and Abbott textbook is great for this, but also look at http://www.amazon.com/Dynamical-Systems-Neuroscience-Excitability-Computational/dp/0262514206/ and even check out the journal Biological Cybernetics. Bertil Hille's book is also really good for things happening in and around the neuron.

This is how I understand the two most popular out there:

Principles of Neural Science (4th edition) has been the gold standard of neuroscience textbooks. It's been called the "bible" of neuroscience and a great jumping off point for anyone who wants to get a very technical and medical perspective on the various functions of the brain. The fifth edition is set to come out this October so I don't know if you'd want to wait or jump into this one but from what I understand this is the number 1.
http://www.amazon.com/Principles-Neural-Science-Eric-Kandel/dp/0838577016

The other textbook that is popular, that I've read most of, is Neuroscience: Exploring the Brain (3rd edition). This textbook makes a lot of the topics that you'd like to learn about organized and easy to understand. While this book probably doesn't go into as much detail as Kandel's, it is a wonderful jumping off point to learn a lot of the basics about neuroscience and to get a solid understanding of a lot of mechanisms controlled by the brain. I highly suggest this one if you're new to neuroscience and not in medschool.

http://www.amazon.com/Neuroscience-Exploring-Mark-F-Bear/dp/0781760038

My focus is theoretical neuroscience, but even still the best resource I found was taking an actual class. I took Introductory to Neuroscience from UC Extension in California. Other then that I also followed the course material from: http://redwood.berkeley.edu/wiki/VS265:_Neural_Computation

I also have read:
Principles of Neural Science, Fifth Edition (Principles of Neural Science (Kandel)) Although, I think it would be dense to start with that.

I really like the book from my introductory course:
Neuroscience: Exploring the Brain, http://www.amazon.com/gp/product/0781760038/ref=wms_ohs_product?ie=UTF8&psc=1

The coursera course on Computational Neuroscience was interesting and if you are CS I highly suggest it as a way to get interested in the field.

Other then that I use Google scholar search to find papers about subjects I am interested in and read those. Currently doing a lot of reading in spare representation.

I entered Neuroscience not really knowing much about programming and now some 8+ odd years later I have two companies willing to pay me six figures for software I've written (mostly est. off royalties but 5 figs. up front). So I've gone through pretty much every stage of expertise there is. For most people, Matlab is sufficient and this book exists which I haven't personally used but looks great. Python may or may not be a great investment. Matlab dominates systems neuroscience so if you go into a 'Matlab lab' that's all you'll use and while it will be nice to have some Python expertise you won't actually use it. On the other hand, there is a movement to use non-Matlab software (more so in Europe) and the stuff in Python is really good. There is a big Python community and a lot of people just like it (and have come to dislike Matlab).


But want to get serious? Learn C and C++. There is simply no substitute for these if you want to write fast, standalone applications. In addition there's enough code, usually in critical applications tied to hardware, written in either of these that it is very good to know in case you have to go in and look/fix. So for the second reason my recommendation would be to learn C.

Scholarpedia (basically peer reviewed Wikipedia) has a great article on the neural correlates of consciousness. That would be a good place to start. I suppose the dominant view is that consciousness is an emergent property which arises from complex patterns of neurodynamic activation spread across synchronously firing (i.e. phase locked) neurons widely distributed throughout the brain. Researchers are currently trying to find what are the bare minimum parts of the brain that have to be activated in order to give rise to consciousness, but its still a bit of a mess.

Probably one of the best researchers in this area is Christof Koch. He just came out with a new book this year called Consciousness: Confessions of a Romantic Reductionist which covers the history and state of the art of research on the neural basis of consciousness in an accessible way. He wrote that scholarpedia page above. He posts most (all?) of his publications here

You might also be interested in the work of Evan Thompson, who was involved in developing and promoting the idea of neurophenomenology as a research method for finding the biological basis of consciousness. His book Mind in Life: Biology, Phenomenology, and the Sciences of Mind outlines the theories he has developed by combining research and insights from cognitive neuroscience, biology, phenomenology, and dynamical systems theory. The book is very unique and utterly fascinating.

Some other people to look into:
David Chalmers, Ned Block, Daniel Dennet, Stanislas Dehaene, Giulio Tononi, Wolf Singer, Antonio Damasio, Francisco Varela

EDIT: This 2003 Nature commentary article from Christof Koch and Francis Crick is actually very informative, despite being 10 years old.

Also, here is a link to Ned Block's 2005 paper in which he discusses his theory of a distinction between what he calls "phenomenal consciousness" and "access consciousness," amongst other things.

I'll try to address your questions first and give general advice at the end.

> many of these expressions have a summation of delta functions over index k. One major problem I have is that I do not know how far back my window should go when considering previous spikes. Should it just be my time increment dt=0.1ms? Or more?

This is often up to the modeler, but Dayan & Abbott's textbook has a section comparing the pros and cons of computing for single spikes vs. sliding windows vs. full history. One reasonable first approach would be to find out how long it takes for a single spike event to decay to the point of being neglible (say, 1/100th of total depolarization) and use that as your window size.

>Another issue I'm having is that I'm confused by what they mean by w+ and w- when talking about Hebbian learning. Are these fixed values?

I think w^+ is the upper bound on weights, w^- is the lower bound. They're using a non-normalized scheme where w^+ or w^- is compared against 1 to determine synapse strength - w < 1 means depression, w > 1 means potentiation.

> Also, why does the expression for I_GABA not have any dependence on w_j? Shouldn't there be some reliance on synaptic connectivity between presynaptic and postsynaptic neurons?

I'm not sure how the weights are being folded into the input current equations, but it's possible that I_GABA isn't affected by synaptic strength - they could compute each input current individually and scale them based on weights, for example.

---------------------


This definitely isn't a beginner-friendly model or paper. Are you recreating as part of a class project, or for a lab? Don't be shy to ask colleagues for help, or even your PI (just make sure you know exactly what you're going to ask and why.) If there isn't a harsh time constraint, I'd recommend checking out a textbook or some other modeling papers from the same lab, and/or citations from this paper.

One thing that experimentalists often have trouble with when trying to reproduce a model is that modelling is not an exact science. You're allowed to mess around with equations, parameters, thresholds and windows to make it work. For every clean equation in the paper, there are 3 or 4 very ugly equations or hacks making the graphs look pretty...it's not ideal, but that's the way the field is and has been for a long time. The point being - keep trying different things until it works. If you're close to the original model, great. If not, find out what new feature in your model makes it work, and see if you can find where the original model addressed that problem.

Good luck!

This book is explicitly named as the book I am required to know, cover-to-cover, including all appendices for my candidacy exam. I have been going through it and it gives quite a broad overview of the field, but also has a lot of detail needed for a good overall knowledge of neuroscience. This book, in addition to your specific readings for your area of neuroscience, should give you all you need to do well in your program and your candidacy exam.

I am also doing my PhD in neuroscience, focusing on Prions and neurodegenerative diseases.

If you have any other questions, feel free to PM me and i'll do my best to answer them!

I’m minoring in neuroscience but my major is biochemistry. If you’re like me and are interested in more of the cellular/molecular aspect, the textbook we use is brilliant! I’ve never seen such complex topics discussed in a more accessible way. And the images are dope. Highly recommend this textbook.

Neuroscience: Exploring the Brain Fourth, North Americ Edition by Bear PhD, Mark F., Connors PhD, Barry W., Paradiso PhD, Mich (2015) Hardcover https://www.amazon.com/dp/0781778174/ref=cm_sw_r_cp_api_i_DLbLDbZZR5MFN

It seems like there are lots of well written books lately exploring popular neuroscience topics from different angles. I might recommend Behave, which has some nice primers in the appendices. And Sapolsky is just a great communicator.

https://www.amazon.com/gp/aw/d/0838577016/ref=mp_s_a_1_2?ie=UTF8&qid=1517968463&sr=8-2&pi=AC_SX236_SY340_FMwebp_QL65&keywords=principles+of+neural+science&dpPl=1&dpID=51aom9pcslL&ref=plSrch

Principles of Neural Science by Kandrl et al.

As someone who studies neuro and works in neuro I can safely say this tome is my bible. It is huge, 37 bucks new because it is an old edition, one of the few textbooks I'd call well written, and has huge listings of primary lit to read for each chapter. It's not primarily for entertainment like most of the books I've seen put here. Man, it is worth its substantial weight in gold. If you actually want to learn, and a diverse amount in the field, get this. If you ever get stuck on points the internet is a truly amazing resource. However, this book is so well written I doubt that will happen often.

When i was an undergrad, i really enjoyed learning the fundamentals from this book: http://www.amazon.com/Cognitive-Neuroscience-Biology-Mind-4th/dp/0393913481
It reads really well, has beautiful images and gives a Sound basis for further reading for people with no or not much knowledge in the field.
Good luck!

Neuroscience is increasingly computational, both in the sense of studying the brain as a computer and in the sense of using a computer to study the brain. Learn to use Matlab - I would recommend either MATLAB for Neuroscientists or MATLAB for Psychologists depending on your ability and interests. Knowing programming and learning techniques early on is incredibly valuable. Volunteer in labs and learn these things, get excellent marks and get stellar recommendations. If you do this you should be fine.

EDIT: MATLAB for Neuroscientists is a bit more technical in nature and will require some exposure to calculus and linear algebra. The more complex bits will also likely require some familiarity with differential equations.

First, on your broader point, you may want to look for programs that stress first-year rotations. I had a BME background, and now do neuroscience related research for my PhD, and joining a department that didn't force me to immediately join a lab was key. I second neuro_exo, it's hard to imagine a top university that won't have multiple people studying the areas you're interested in.

On your more specific question, what sort of math you should review depends on the sort of neuroscience you're talking about.

If you're referring to theoretical neuroscience/modeling, Dayan and Abbott is a standard reference. It includes the broader neuroscientific context as well as the math, so it's quite rewarding to read.

If you're talking about motor neuroscience/learning, a lot of the ideas derive from linear algebra and controls. Watch a few machine learning lectures, review those topics and you should be set.

A lot of the new ideas/excitement has recently focused on techniques to handle high dimensional datasets (see some of the discussion behind the BRAIN initiative). This gets into some rather complex math pretty quickly, so there's not too much I'd directly recommend, except that you check out recent papers in the field to see what you'd need (there's typically a lot of dynamical systems work here).

Most of the rest of neuroscience does use a fair amount of math, but they what it uses tends to be very vague/operational. You'll do a lot of signal processing, a lot of digital filtering/averaging, and noise reduction will be a major focus. Review your EE class notes to get set for this.

Edit: This was coincident with neuro-exo's response. I agree with everything he/she said.

Scholarpedia is probably a good starting point for you if you are looking for a website which provides a fair amount of detail. It is like Wikipedia but it is edited by neuroscientists (Wikipedia is also often a good starting point if you want to understand some topic better).

If you want a must-read you need to narrow your focus more. Are you looking for something on medical neuroscience, cognitive psychology, biochemistry, computational neuroscience...? Are you looking for something for a layman or something more technical? Given that you are a high school student you will probably want to start with something layman-ish which doesn't make you frustrated with not knowing the technical details.

Books are often of higher quality than websites because they are edited and you can often find user ratings on them (see for example this book).

The top comment is right that the Kandel is a great neuroscience text. I have it myself and it's my go-to. If you're starting out from the bottom and learning on your own I would suggest a more user-friendly text.

Neuroscience: Exploring the Brain is good. I have the 3rd edition, which has probably been updated by now. Looks like the price is very manageable, too.

“Foundational Concepts in Neuroscience” by David Presti (UC Berkeley professor) was a great book for me. I’m a computer scientist with very little advanced biology background and I found the material to be very well laid out and interesting to read.

https://www.amazon.com/dp/0393709604/

Kandel is a very comprehensive neuroscience textbook with a lot of good figures as well as descriptions of experimental evidence. The most recent version came out just last year, so it is very current.

Purves also contains excellent figures but concepts are delivered on a more basic level, probably better suited for undergraduate/non-research perspective.

The textbook Neuroscience: Exploring the Brain by Mark Bear could be a great start: https://www.amazon.com/Neuroscience-Exploring-Mark-F-Bear/dp/0781760038. Some books for the general public such as David Eagleman's The Brain or Rita Carter's Mapping the Brain are good too (although they might be too easy for you given that you are a med student).

As for brain mapping, not sure if you are talking about connectome or the Blue Brain Project?

Anything from Oliver Sacks always worth reading imo.

• Neuroscience: Exploring the Brain!
  
  
• The Compassionate Brain: A Revolutionary Guide to Developing Your Intelligence to Its Full Potential: How Empathy Creates Intelligence!
  
  
• Descartes' Error: Emotion, Reason, and the Human Brain!
  
  
• Affective Neuroscience: The Foundations of Human and Animal Emotions!
  
  
• In Search of Memory: The Emergence of a New Science of Mind!
  
  
• The Man with a Shattered World: The History of a Brain Wound!
  
  not really neuroscience, but interesting imho:
  
  
• How the Mind Works!
  
  
  *(hopes formatting works)

Kandel's Principles of Neural Science is good. Pdf available online. Concussion falls under traumatic brain injury. I have a friend who did her honours in this field. Worked under a prof named Ramesh Rajan at Monash university, you might want to check him out. Awesome guy. Just as a heads up, you will most likely be working with rodent models in TBI.

This stuff is mentioned in the book Behave: The Biology of Humans at our Best and Worst by Robert M. Sapolsky. I'd recommend reading it. Not advertising it either, I'm aware Reddit has advertisers for stuff.


I'll link the book in case you may want to give it a look at, what he covers is great, along with environmental disadvantages/advantages to brain development:


Behave: The Biology of Humans at Our Best and Worst https://www.amazon.com/dp/1594205078/ref=cm_sw_r_cp_apa_i_Nq9uDbKBD0Z4R

Sure, I'd start with Neuroscience for an overview, some more cognitive related computational systems are described in Cognitive Neuroscience, more advanced ones in Vision and Finally Rhythms of the Brain, you can then branch out further into language and memory.

​

https://www.amazon.com/Neuroscience-Dale-Purves/dp/1605353809/ref=sr_1_6?keywords=neuroscience&qid=1563645060&s=gateway&sr=8-6

https://www.amazon.com/Cognitive-Neuroscience-Biology-Mind-Fifth/dp/0393603172/ref=sr_1_2?keywords=cognitive+neuroscience&qid=1563645142&s=gateway&sr=8-2

https://www.amazon.com/Rhythms-Brain-Gyorgy-Buzsaki/dp/0199828237/ref=sr_1_1?crid=813HTOI3332A&keywords=rhythms+of+the+brain&qid=1563645094&s=gateway&sprefix=rhythms+of+the+%2Caps%2C199&sr=8-1

https://www.amazon.com/Vision-Computational-Investigation-Representation-Information/dp/0262514621/ref=sr_1_1?keywords=vision+neuroscience&qid=1563645008&s=gateway&sr=8-1

What kind of paper? Don't you have access to most of the journals through your university?
I can browse many journals at home with VPN-Access, provided by my university.
For books, try these:
http://www.amazon.com/Principles-Neural-Science-Fifth-Kandel/dp/0071390111/ref=sr_1_1?ie=UTF8&qid=1452278840&sr=8-1&keywords=principles+of+neural+science
OR (less detailed)
http://www.amazon.com/Neuroscience-Exploring-Mark-F-Bear/dp/0781778174/ref=sr_1_1?ie=UTF8&qid=1452278819&sr=8-1&keywords=neuroscience

I heard some rumours that at least the Kandel is available as a free PDF in the internet, just use google with the proper terms ;)

This is a pretty good book, also written for undergrads with only a basic bio background.

Don't worry about being smart enough. Just work hard and study hard. If you're looking for a good book try Neuroscience: Exploring the Brain. If you find this to be too dificult then make sure to brush up on some general biology. If you're particularly ambitious then you can try Kandel's Principles of Neuroscience (This is a graduate level text, but it has a lot of information and covers a wide breadth of Neuroscience).

Spikes: Exploring the Neural Code is a great book for people from a physics background who want to learn neuroscience.

Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts https://www.amazon.com/dp/0670025437/ref=cm_sw_r_awd_Q6Qzub003NK0X

Comprehensive, current, a generally pleasant read/listen.

Neuroscientist here who studies information.

Check out Fred Rieke and Bill Bialek's book "spikes." One of the best resources: http://www.amazon.com/Spikes-Exploring-Neural-Computational-Neuroscience/dp/0262681080/ref=sr_1_1?ie=UTF8&qid=1323961329&sr=8-1.

Izhikevich has a great book for mathematical descriptions of neurons and circuits, some cartoons and interpretable descriptions
https://www.amazon.com/Dynamical-Systems-Neuroscience-Excitability-Computational/dp/0262514206/ref=sr_1_1?ie=UTF8&qid=1543300294&sr=8-1&keywords=dynamical+systems+in+neuroscience
Edit: spelling

This is the textbook you will likely read your first year, so you might as well look at it and see how it makes you feel: https://www.amazon.com/gp/product/0071390111/

It's somewhere online in nice PDF format too.

At a first year student ‘Neuroscience- exploring the brain’ has been recommended if that helps

Neuroscience: Exploring the Brain was my go-to when learning neuroscience coming from an engineering and physics background. It's pretty accessible for people from many different backgrounds. For example, it covers the requisite biology and chemistry you'll need to understand the basics if you haven't had exposure to those fields yet.

Blumenfeld's book is generally really good for that kind of stuff.

Sounds like you need this book. It’s fairly plain language and cheap!!

Buzsaki's book is also a good general reference for this:

http://www.amazon.com/Rhythms-Brain-Gyorgy-Buzsaki/dp/0199828237

I second Oliver Sacks - Hallucinations or this oliver sacks book. Also "Tale of Dueling Neurosurgeons" is good and provides a more general overview

Connectome by Sebastian Seung is good

​

Depending on your girlfriend's preferences, this could be cool:
http://www.amazon.com/Phi-A-Voyage-Brain-Soul/dp/030790721X

Theoretical neuroscience. Check out this textbook: http://www.amazon.com/Theoretical-Neuroscience-Computational-Mathematical-Modeling/dp/0262541858

You should also look into the Redwood Center for Theoretical Neuroscience (at Berkeley) and at http://www.cns.nyu.edu/wanglab/ if you have the chance.

An Introduction to the Event Related Potential Technique is the book I recommend to everyone starting out in EEG. It answers the questions you never even realised you had.

Edit: You did say online, but this book is fairly cheap to buy (for a textbook, anyway).

Edit edit: Actually, do you mean ERPs or clinical EEGs?

Tononi's Phi

Robert Sapolsky's relatively recent book, "Behave".

It is phenomenal.

He addresses this tons in his book Consciousness.

http://www.amazon.com/gp/aw/d/0878936130/ref=mp_s_a_1_1?qid=1451446323&sr=8-1&pi=AC_SX236_SY340_FMwebp_QL65&keywords=blumenfeld+neuroanatomy

It is only natural for researchers with vested interests in different levels of analysis - in this case, more abstract computational models that ignore the molecular and subcellular levels of detail, even the cellular level entirely (with point process neuronal models, for example) - to be opposed to so much funding going into the HBP, which inherently is geared towards simulating even the smallest functionally relevant level of analysis (viz., the molecular). This open letter is a window into the general phenomenon of competing visions and paradigms, only amplified because the stakes are so much higher (1.2 Bn Euro higher, to be exact).

On the one hand, I agree that more independent review would be helpful in order to stop some of the more un-scientific moves that the HBP has been taking in terms of letting go of people who do not "toe the line", as outlined here. On the other hand, there would be a downside to independent review as well, in that ideological differences from the reviewers may unnecessarily stifle the project. This is a problem with the reviewing process in most journals, in fact, so in that sense, nothing new there.

From my point of view, I believe that the framing of this debate in terms of the amount of money being "only invested in one person's vision" is misleading and avoids the bigger picture. The fact remains that we do have too much neuroscientific data, and the research & funding structures are geared so as to encourage little bite-sized bits of research that demonstrate some effect of one molecule, or modulation of a synapse, or any similar isolated aspect of the nervous system - i.e., towards "quick returns". True, newer tools like optogenetics are allowing for larger-scale investigations into the nuances of function of entire circuits, but even then, the brain is complex enough that the story of any individual opto paper is inherently narrow and limited. We do need to integrate all of this data, and what better way than to throw it all into one big computational simulation that doubles up as a data repository?

The HBP project aims to be a "service provider" as discussed in the BBC article linked to above. Even in computational neuroscience, where there is fierce debate as to appropriate levels of analysis of study and therefore understanding of brain function - there is no debate as to the fact that neurons do operate on a molecular level. This huge diversity of neurotransmitters, ion channels, cell types, even glial cells (groan, cries almost every neuroscientist who realizes that we can't continue to ignore them) has evolved for a reason, and each one has shown to have some kind of functionally relevant role to a neuron, circuit, and therefore behaviour. So whatever abstract models we use in our pet studies, must necessarily bottom out at the lowest level of detail in order to be relevant to understanding of the actual brain. Otherwise, we are no better than armchair philosophers trying to understand how the brain works. You need to examine the actual product of evolution, the actual tissue itself - the very nuts and bolts - and understand it at that level.

No, the HBP will never be complete, and no, it will probably be grossly incorrect in many, many ways - because important facts about the brain are not known and remain to be discovered. That shouldn't stop us from starting somewhere. As Markram says, sure, we can invest all this money into the usual ecosystem of research. But that will ultimately generate another few hundred isolated and entirely independent papers with more data, but no more integrated understanding of the brain.

The bottom line is that what is at stake is the question of how best to continue doing neuroscience work. Henry Markram believes (as do many others, let's not forget that - it's not just a "single quirky guy's vision") that some kind of integrated approach that starts to put it all together is needed at some point. It won't be perfect, but we have enough data as it is that it is needed now - in fact, it was needed yesterday. Certainly, it won't even provide all the answers, and it's not meant to. For instance, the criticism of the HBP replicating the entire brain and still not providing any answer about its function is correct in a way. It is indeed silly to think that when the "switch is turned on", the simulation will exhibit (rat) cognition. We need input from the environment, not just to provide data but also to entrain the brain and calibrate its endogenously generated rhythms - just think of the unravelling of the mind that occurs when humans are subjected to sensory deprivation. (For a fuller treatment on this issue of the environment serving to entrain or calibrate the brain, see Buzsáki's excellent treatise, Rhythms of the Brain).

What the HBP will provide, however, is a repository for integrating the swathes of data we already have, and a framework for testing any ideas of the brain. No, it will never be complete, but it is badly overdue, and thoughts of continuing to live without an integrating framework that can be tested, prodded, and drawn upon - instead continuing each researcher's narrow pet projects in isolation from one another - is as past folly as it would be to pretend to be studying and understanding genetics without having the entire genome sequenced.

In that sense, the HBP can only help in any and all endeavours in understanding the brain by providing that baseline model with as much cellular and molecular detail incorporated as possible, because any higher levels of analysis will ultimately have to interface with it (or at least with the level of detail the HBP is aiming to capture) in order to show ultimate relevance in terms of the brain. The brain, as a biological system, is inherently different in nature than the phenomena that many computational neuroscientists (coming as they do, mostly from physics and engineering backgrounds) are comfortable dealing with - which is in the framework of physical systems that can be described with a handful of equations that summarize the overall complexity at hand. The brain, sadly, is not such a system and is not amenable to "spherical cow" levels of analysis. That's not to say that it cannot be done, and that no fruitful results will emerge from such studies. On the contrary, we can learn many useful facts about the brain by building and analyzing simplified models. It's just that inherently, any such endeavours will miss the mark in important ways. The "answer", then, is to stop thinking in terms of a zero-sum game (which is the attitude that signatories of this open letter seem to be coming from) and instead consider it as a joint project or venture. Indeed, the more abstract levels of analysis have been too much in the limelight for many years, without paying any dividends. The connectionist paradigm, started in the 80s, hasn't given us any concrete and large-scale understanding of the brain, and has unfortunately (for our knowledge of the brain but not for commercial ventures) and quietly devolved into machine learning tricks for learning Netflix user preferences, etc.

In fact, such an approach that the HBP is embarking on, is badly overdue, and vastly underrepresented. It's not a popular approach because it accepts the messiness of the brain and doesn't shirk away from it by abstracting it away. Sure, it's a double-edged sword, in that by opening the Pandora's box of the molecular level, you risk missing out on what we do not yet know, but that is part and parcel of any scientific approach. Thus, kudos to the HBP project and Henry Markram for managing to get this kind of project off the ground.

I believe it will only help further our understanding of the brain in an integrated way that can evolve over time and with contribution from other levels of analysis. Those who are opposed to it, in my opinion, are doing so unfortunately primarily on personal and ideological grounds -- i.e., on ultimately selfish and jealous grounds -- than on valid scientific rebuttals.

Sadly, I lack Markram's eloquence and diplomacy in addressing the critics, but sometimes you have to grab the bull by the horns and address the real issue rather than skirt around it and be afraid to step on eggshells (meaning other people's egos).

-- PhD candidate in computational neuroscience, whose own biases have been amply revealed, he hopes.