Reddit mentions: The best neurology books

check out Bengio's paper if you haven't yet. There's a few really cool pieces, but the most relevant I think... the first chunk of the paper looks at a really simple two discrete random variable system, and posits two possible causal models: X -> Y and Y -> X. The thrust of that part of the paper is basically that fitting both those causal models is equally expensive, you've got the same number of model weights after all. The magic happens when you change p(x) for X -> Y = p(x)p(y|x) and do a transfer learning cycle on this new distribution. For the 'wrong' model, you have to refit every single model weight, because the structure of the model isn't captured in a way that separates that causal connection properly, it's distributed through the model instead. For the 'right' model though, he links to another paper showing that the gradient is zero for all the already correct parameters, so you end up just changing N of the underlying change instead of the full O(N^2) parameters of the model. He's got a graph showing convergence for the 'right' and 'wrong' model on the transfer learning objective.... both converge to the same spot, but the difference between the number of samples needed to converge for both is really, really huge. The 'wrong' causal model takes massively more samples to converge. From your very first observed example on the transfer dataset too, the sparseness of that gradient on a transfer objective for the 'right' model was how you could distinguish the correct model even. Your point about parameters needing an update being kept small is right I think... The question though is how to make sure that's reliably the case in general. There's some really cool stuff in disentangled representation learning for RL too I think... I don't know. I guess at this point I'm sold that for a model (in general) to properly isolate the various moving parts of the system instead of representing it through the whole thing in a giant mess, will require a new approach to learning.

Course, that doesn't mean you can't get some of that separation just by carefully controlling the training set and order you see them in. You're completely right that there's some really cool generalization power that can come up with the right training protocol (Learning to Make Analogies by Contrasting Abstract Relational Structure, Emergent systematic generalization
in a situated agent, and ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness come to mind) but I think as long as we're just using our current dumb AI systems with a carefully manicured training protocol, we're missing a huge piece of the puzzle. We'll always need some level of curriculum management of course (humans obviously benefit from the right training material too) but I'm convinced enough that the ability to explicitly form a proper disentangled representation of the environment is key that I'm heading in that direction in my studies. Guess we'll see in a decade whether or not I regret my focus, haha.

And yeah, I think there's a ton of insight to be gained from studying biological consciousness. I actually started scrapping my way into that six months ago or whatever on the side. I'm currently 600 pages into Kandel's beastly 1,700 page 'principles of neural science'. All that's just preliminary biology stuff, but there's some really cool looking books I want to hit when I get a little farther in. this book especially is one I'm excited to hit next when I'm done with Kandel, it looks like it's doable without a ton of background in neuro, sounds like you might enjoy that one too. Beyond that, check out Jeff Hawkins 'on intelligence' (and the research of his group if you're interested in what he has to say... cool stuff there about cortical column functioning as a building block of cognition) and Christof Koch's 'Consciousness: Confessions of a Romantic Reductionist'. Both of those two books are just for lay people, so you could blow through them real quick to get a flavor of their ideas, but that last one especially... Koch seems to be involved in the only (that I've found so far) falsifiable model of consciousness. It has to do with information integration between disparate parts of a system... really cool sounding ideas, but the math is absolutely beastly in the theory itself, haha. I'm not equipped yet to weather it, but it seems like there's some really important ideas there too from what little I've grasped so far.

Anyway, yeah... totally agree. Might as well take inspiration from the one working example of a strong AI system we have access to, haha.

> I know you said you have a book or something - PM me about that, I'm interested!

I've sent you a PM about the book. Glad to hear you're interested!

> What about this theory that floats around on the internet and that celebrities like Joe Rogan talk about that the pineal gland produces DMT, especially during sleep.

There is some evidence of DMT production in the pineal gland, but it's very scant at this stage. One study, last year (2013), showed trace amounts of DMT in the pineal glands of rats. This could mean that DMT is produced there, or somewhere else in the body and then stored/used there; however the amounts were far too limited to have any kind of psychedelic effect.

It's not extremely surprising, as DMT is chemically quite similar to the likes of serotonin and melatonin, so for it to form naturally in the brain isn't a huge jump biochemically speaking... it's also however not terribly interesting or useful until we know more about how much, when, why, and so on.

It could also however simply have been a freak occurrence. I'd like to see more studies being done to confirm it - especially with multiple species and animals of different ages (which may make a very large difference as well given the possible relationship between the pineal gland and the parietal eye that I mentioned).

> How did you learn as much as you know specifically about LSD?

It helps being old ;)

More seriously - I've simply read a lot and studied a lot with a critical mind. I'm a software developer professionally, but I've spent around 15 years of my free time learning and researching psychedelics and associated fields. I have no formal training, but I read university level textbooks on neuroscience, biochemistry, pharmacology and so on for fun.

Mostly, I'm just the kind of person that's both passionately curious about the world as well as being the kind of person that likes to critically analyse things. This helps to steer away from the mystical side of things (all very interesting, but lacking in anything even remotely similar to evidence) and keep me searching in more productive lines of enquiry.

> Any other books or references you'd be willing to share?

Hmmm... quite a lot.

"LSD" by Otto Snow is a good general purpose LSD book, with pretty detailed synthesis information that helps you understand the chemistry even if you're not actually planning on synthesising it yourself.

I'm not sure of your current level of skill, but if you need an intro, or refresher in to the basics of the right kind of chemistry, then Organic Chemistry I for Dummies is a great book according to my wife (who went from "no knowledge" to "able to at least understand what I'm talking about" just from this book).

For a "step-up" from there and getting to looking at the brain specifically, I'd start with (and continually go back to) Molecular Neuropharmacology: A Foundation for Clinical Neuroscience.

Aside from that, every research paper you can find dealing with related material. There's some good review papers as well for "summing up" a lot of others. One I really liked was "The Pharmacology of Lysergic Acid Diethylamide: A Review" by Passie et al.

I also found some online courses to be really good. I recently did "Drugs and the Brain" on Coursera; it was definitely a good refresher for me, and would be excellent for anyone with a basic grounding but wanting to learn more in general. There's another on Coursera called "Medical Neuroscience", which I unfortunately missed, but will catch the next time around; and one coming up really soon titled "Understanding the Brain: The Neurobiology of Everyday Life" which I'll be doing but expect to be a somewhat simpler course than the others (I'll take it anyway - re-covering basics is always good because you do find things you've managed to miss no matter how long you've been learning).

Edit: One additional thing I should have mentioned... here on reddit, check out /r/drugnerds and maybe also /r/rationalpsychonaut

As to arguments supporting free will I will suggest based on some of the references cited below that it is very hard to support the idea that we live in a purely deterministic universe.

Based on the 'meditative perceptions' of the Buddha the concept of interdependence and non-self emerged. I interpret interdependence as emergence, and non-self as the separation of consciousness from the concept formed in the mind as self.

The perceptions arising during mediation circumvent and trump the conditioned and conceptual responses of our cortex/mind, greatly expanding the parameters of what we could consider our free will.

see also Shulman, Eviatar. Rethinking the Buddha: Early Buddhist Philosophy as Meditative Perception Cambridge University Press. Kindle Edition.



A response to your comment, with some references..."I could understand that a neural network may have an internal mechanism of information flow which is so complicated that it exceeds our current level of knowledge in being able to map or understand it (the complexity of the connections create consciousness and we aren't advanced enough to understand this?).'

The efficacy of neural networks applied to AI is that the repetition of simple units or Boolean nodes-on/off, allows complexity to arise from very simple uncomplicated structures.

>This new science centers on the study of “coupled oscillators.” Groups of fireflies, planets, or pacemaker cells are all collections of oscillators—entities that cycle automatically, that repeat themselves over and over again at more or less regular time intervals. Fireflies flash; planets orbit; pacemaker cells fire. Two or more oscillators are said to be coupled if some physical or chemical process allows them to influence one another. Fireflies communicate with light. Planets tug on one another with gravity. Heart cells pass electrical currents back and forth. As these examples suggest, nature uses every available channel to allow its oscillators to talk to one another. And the result of those conversations is often synchrony, in which all the oscillators begin to move as one.

>As he considered increasingly homogeneous populations of oscillators, no sync occurred until he reached a critical point, a threshold of diversity. Then, suddenly, some of the oscillators spontaneously locked their frequencies and ran around together. As he made the distribution even narrower, more and more oscillators were co-opted into the synchronized pack. In developing this description, Winfree discovered an unexpected link between biology and physics. He realized that mutual synchronization is analogous to a phase transition, like the freezing of water into ice. Think for a moment about how astonishing the phenomenon of freezing really is. When the temperature is just 1 degree above the freezing point, water molecules roam freely, colliding and tumbling over one another. At that temperature, water is a liquid. But now cool it ever so slightly below the freezing point and suddenly, as if by magic, a new form of matter is born. Trillions of molecules spontaneously snap into formation, creating a rigid lattice, the solid crystal we call ice. Similarly, sync occurs abruptly, not gradually, as the width of the frequency distribution is lowered through the critical value.

>Strogatz, Steven H.. Sync: How Order Emerges from Chaos In the Universe, Nature, and Daily Life (p. 54). Hachette Books. Kindle Edition.

To elaborate on the microbe I will refer to a description of slime mold found in 'The self-organizing Universe' by Erich Jantsch

When food is abundant there are a group of single celled organisms that go about their individual lives in the forest floor. When hard times occur these organisms start 'randomly' emitting ATP.

Suddenly populations of these organisms start to come together - 10,000 to 100,000 individuals form a worm. Cells with high cellulose form a foot, and high sugar form a mouth/head. This worms then crawls through the forest until it finds a good food source and then it dissolves and all the single cells go about their lives again. Complexity arising from the organization of the chaotic and unpredictable behavior of simpler units.

A couple more references...

>What makes the Prigoginian paradigm especially interesting is that it shifts attention to those aspects of reality that characterize today’s accelerated social change: disorder, instability, diversity, disequilibrium, nonlinear relationships (in which small inputs can trigger massive consequences), and temporality—a heightened sensitivity to the flows of time.

> The work of Ilya Prigogine and his colleagues in the so-called “Brussels school” may well represent the next revolution in science as it enters into a new dialogue not merely with nature, but with society itself.

> The ideas of the Brussels school, based heavily on Prigogine’s work, add up to a novel, comprehensive theory of change. Summed up and simplified, they hold that while some parts of the universe may operate like machines, these are closed systems, and closed systems, at best, form only a small part of the physical universe. Most phenomena of interest to us are, in fact, open systems, exchanging energy or matter (and, one might add, information) with their environment. Surely biological and social systems are open, which means that the attempt to understand them in mechanistic terms is doomed to failure. This suggests, moreover, that most of reality, instead of being orderly, stable, and equilibrial, is seething and bubbling with change, disorder, and process.

>In Prigoginian terms, all systems contain subsystems, which are continually “fluctuating.” At times, a single fluctuation or a combination of them may become so powerful, as a result of positive feedback, that it shatters the preexisting organization. At this revolutionary moment—the authors call it a “singular moment” or a “bifurcation point”—it is inherently impossible to determine in advance which direction change will take: whether the system will disintegrate into “chaos” or leap to a new, more differentiated, higher level of “order” or organization, which they call a “dissipative structure.” (Such physical or chemical structures are termed dissipative because, compared with the simpler structures they replace, they require more energy to sustain them.)

>Prigogine, Ilya. Order Out of Chaos (Radical Thinkers) . Verso Books. Kindle Edition.

also

>"Biological theorists who seek to explain consciousness have gotten stuck in the cerebral cortex, citing it as the situs of consciousness, i.e., where consciousness arises. I will challenge this notion and, accordingly, offer a new theory of how we become conscious during various natural or induced states in which we are unconscious." - Pfaff, Donald. How Brain Arousal Mechanisms Work (Kindle Locations 107-110). Cambridge University Press. Kindle Edition. https://www.amazon.com/Brain-Arousal-Mechanisms-Work-Consciousness/dp/1108433332

and

>A new theory is taking hold in neuroscience. The theory is increasingly being used to interpret and drive experimental and theoretical studies, and it is finding its way into many other domains of research on the mind. It is the theory that the brain is a sophisticated hypothesis-testing mechanism, which is constantly involved in minimizing the error of its predictions of the sensory input it receives from the world. This mechanism is meant to explain perception and action and everything mental in between. It is an attractive theory because powerful theoretical arguments support it. It is also attractive because more and more empirical evidence is beginning to point in its favour. It has enormous unifying power and yet it can explain in detail too. This book explores this theory. It explains how the theory works and how it applies; it sets out why the theory is attractive; and it shows why and how the central ideas behind the theory profoundly change how we should conceive of perception, action, attention, and other central aspects of the mind.

>Perception, action, and attention are but three different ways of doing the very same thing. All three ways be must be balanced carefully with each other in order to get the world right. The unity of conscious perception, the nature of the self, and our knowledge of our private mental world is at heart grounded in our attempts to optimize predictions about our ongoing sensory input.

>The theory promises not only to radically reconceptualize who we are and how aspects of our mental lives fit into the world. It unifies these themes under one idea: we minimize the error between the hypotheses generated on the basis of our model of the world and the sensory deliverances coming from the world. A single type of mechanism, reiterated throughout the brain, manages everything. The mechanism uses an assortment of standard statistical tools to minimize error and in doing so gives rise to perception, action, and attention, and explains puzzling aspects of these phenomena. Though the description of the mechanism is statistical it is just a causal neuronal mechanism and the theory therefore sits well with a reductionist, materialist view of the mind.



>Hohwy, Jakob. The Predictive Mind, Oxford University Press. Kindle Edition.

How can free-will not exist in a brain that is constantly creating choices?

> Or can someone shed some light on what they're discussing and what this paper is proposing?

1. Consciousness (atleast, consciousness(es) that we are familiar with) seems to occur at a certain scale. Conscious states doesn't seem to significantly covary with noisy schocastic activities of individual cells and such; rather it seems to covary at with macro-level patterns and activities emereging from a population of neurons and stuffs. We are not aware of how we precisely process information (like segmenting images, detecting faces, recognizing speeches), or perform actions (like precise motor controls and everything). We are aware of things at a much higher scale. However, consciousness doesn't seem to exist at an overly macro-level scale either (like, for example, we won't think that USA is conscious).
   
   
   
2. The authors seem to think that the reason consciousness exists in this scale because of the property of 'non-trivial information closure'. A system is informationally closed if the information flow from environment to the system is 0. A trivial case of information closure is when the system and the environment is pretty much independent. For the authors, the degree of consciousness is instead associated with the degree of closure in non-trivially closed informational systems. What is 'non-trivial information closure'? - in this case, even though the environment at time t (E_t) plays a role in the formation of the system state in time t (Y_t), Yt encodes enough information about itself , the environment, and the 'environment's influence on itself', that it is possible for the system to predict much of (not necessarily everything) Y{t+1} just on the basis of Y_t alone, without accessing E_t.
   
   
   2.5) Rejection of 'trivial information closure' helps a bit with bounding conditions. We can think of an aggregate of informationally closed system as a informationally closed system, but we wouldn't think that a mere aggregate of potentially 'conscious' minds are together having a single unitive consciousness. Since trivial information closure doesn't contribute consciousness according to their hypothesis, adding independent closed systems to another system would not change the degree of consciousness of either. This may also have some relationship with the idea of integration in IIT (Information Integration Theory).
   
   
   
3. (2) can explain why consciousness seem to be associated with a certain scale. It is difficult to make prediction by modeling all noisy schocastic neural-celllular-whatever activities. Prediction are easier if essential informations (including ideas of causation, and such) of the environment are modeled at higher 'coarse-grained' scale (see (1)) (more at the level of population than at the level of samples).
   
   
   
4. You may now wonder, even if predictability from self-representated states can exist in a certain scale which happens to be seemingly associated with consciousness, it's not clear why predictibility is necessary for consciousness, nor it's very intuitive that our degree of consciousness depends on predictibility. For that I don't have any clear answers. Intuitively, most of our conscious experiences does seem to be laden with immediate expectations, and anticipations - even if we don't always explicitly notice it. The so-called 'specious present' may always represent immediate past as retention and immediate potential future as anticipation. But besides that, this framework can have other intuitive properties, like for example, following this framework, high-level contentful consciousness must have a much richer representations (of self and environmental information) with a more complex model that has higher predictive prowess - which would need a more complex neural substrate - which seems to affirm the intuition that 'higher consciousness' would correlate with more 'complex stuffs'. It can also explain differences in conscious and unconscious processing. For example, it can explain blindsight (where people report that they are blind - not conscious of visual information; but behave in a manner that shows evidence that they have some access to visual information) by saying that in this case, the environmental visuation information is more directly associated with actions and such; it is not internally representated in a rich state at a coarse grained level offering predictibility - thus people with blindsight are not conscious of their 'sight'.
   
   
   
5. 'predictions' seems to be the central part of the paper, however it still seems to be lacking in intuition about why. However, there is a decent chunk of literature in cognitive science and stuff related to the relationship with predictive processing and cognition. PP, Prediction Error Minimization and such are recent hot topics in cognitive science and philosophy. These line of works may or may not better support the paper. This paper is aware of the works and discusses it close relationship with them. ICT seems to extend upon PP in distinguishing unconscious predictions, and conscious predictions, and incorporate the idea of scale and the relationship of consciousness and coarse-graining. I don't have much of a background about PP, but works of Andy Clark may be good introductory materials: (For example) https://www.amazon.com/Surfing-Uncertainty-Prediction-Action-Embodied/dp/0190933216/ref=sr_1_1?keywords=andy+clark&qid=1570248756&s=books&sr=1-1
   
   I cannot personally vouch for the book, but Andy Clark is one of 'big guys' in the field; so he can be a pretty reliable source.
   
   
   
   
6. ICT seems to work well with some of the other theories of consciousness too (Global Workspace Theory, IIT, PP), which the authors discuss about in the paper. It seems to fill in some gaps of those theories. But I am not very qualified to judge about that.
   
   
   _____
   
   
   About background materials. It seemed pretty readable to me without much of a background. For statements about neural activties, I am just taking their words for it, but the citations can be places to look. You can find more about phenomena like 'blindsight' from googling, if you weren't already aware of it. As opposed to the recommendations made by the other redditor, I don't think it has much to do with anything related to the hard problem of consciousness (Nagel's Bat or Chalmer's zombie) at all and you don't need to read them for this paper - though they can interesting reads for their own sake and can help better understanding the potential limitations - but these work goes on a more philosophical direction not quite related to the scope of the paper. The equations may have some relation with information theory (again the citations may be the best bet for better background). PP seems to be most closely related to the paper with the idea of predictability being on the center. So that may something to explore for background. IIT can be another background material for this: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003588
   
   https://www.iep.utm.edu/int-info/

I'm not a chemist; I'm currently studying ChemE at university. I'm also the "science mod" over on /r/trees, so I think I can help out a bit with this one.

The science behind cannabis and how it works is extremely interesting, but it won't help you with 99% of Biochemistry.

Everything we know about cannabis can be learned pretty quickly, provided you have the backgrounds in chemistry, biology, and preferably a bit of pharmacology.

What you can is do is study drugs and their functions as a whole to supplement and enhance your studies in biochemistry; I know that it's granted me a new appreciation for the human body and the processes that regulate it. These fields are vast and expanding at an astonishing rate.

The field of pharmacology is huge, but in a nutshell you can break it into pharmacodynamics and pharmacokinetics. They focus on effects and the relations between dosage and response (dose response curves, etc.) as well as the mechanisms through which the drug is processed and how the drug passes through the body, respectively.

In short, pharmacokinetics studies what the body does to a drug, while pharmacodynamics studies what the drug does to your body.

As a Biochemistry major, these topics will likely be right up your alley. You'll still have to do the mundane, but perhaps some background along these lines will provide you with a new perspective on the processes you are studying in class.

If this sounds like your thing, I'd recommend the following text, provided you already have a good grip on molecular biology and a little electrochemistry: Molecular Neuropharmacology: A Foundation for Clinical Neuroscience

If you like this text or you just want something to supplement it, Caltech, easily one of the top research universities for this field, offers a course taught by Dr. Henry Lester via Coursera, here.

It's a highly informative course that pairs very well with the text I linked above. You'll touch on everything from drug addiction to recreational drugs to the different receptors and how they are activated.

It's not active right now and I'm not sure when the next session will be, but you can go onto Coursera and watch Professor Lester's lectures which are, by far, the most integral part of the course. I went through it last session (January - February) and I was very satisfied with both the material and the way it was presented.

Tl;dr: You can supplement your classroom material with all sorts of interesting studies related to drugs, but if you're not interested in the material you're studying in class at all, it may be time to rethink your field. You can't tie everything to drugs, but you can use the study of drugs to enhance your appreciation for the "macro" systems you're studying.

> Basically, just find a way to simulate a couple million cortical columns.

You should not confuse a "column" in HTM with a "cortical column" from a real mammalian cortex. We have reason to believe that a single cortical column is a feature detector. A cluster of columns act over milliseconds to suppress other, similar columns in a competitive manner. Inside real brains, a single cortical column is a highly interconnected group of neurons. (mostly fully connected).

You could take several hundred feature detectors as cortical columns. Then you can create singular cells that index a connection between two of them at a time. That is, the connecting neuron is active only when both of its constituent columns are active. These neurons that "index" two columns are called cortico-cortical neurons or CCNs. After the columns have "Settled down" from re-entry, the remaining active columns indicate the presence of a feature in the perceptive stimulus. The CCNs will be active if-and-only-if both of their indexed columns are still active. Let the number of columns be N. Then the number of CCNs is (N^2 - N)

Now for the tricky part. You index each CCN with a hippocampal neuron. The hippocampal neurons are in a fully-connected hopfield network. Given that a percept is a collection of co-occuring features, then the hopfield network will learn a robust pattern after exposure to it, as a collection of co-firings of the active CCNs. Here is a toy diagram of what you are trying to accomplish:



http://i.imgur.com/MSRwT4C.png


> Reward system : Nearly universally, learning is based on rewarding correct behavior and answers. How well would an infant learn that it needs to eat if it felt no hunger? How well does a neural network learn without some type of fitness system to keep it on track? For AI, you need some type of reward system.

This was already done by Rolf Pfeifer, Gerald Edelman, and again by Jeffrey Krichmar. In all cases, they did not use HTM networks or even "deep belief nets". Instead they modulated connections between various networks. Each network was associated with a modality and it was structured as a SOM, or Kohonen Self-Organizing Map.

• A diagram of Pfeifer's agent http://i.imgur.com/bTTUlAc.png
  
  
• Krichmar's book http://www.amazon.com/Neuromorphic-Brain-Based-Robots-Jeffrey-Krichmar/dp/0521768780
  
  
• Tutorial on SOMs. Make sure to click the barely-visible blue numbers at the button to move to next page. http://www.ai-junkie.com/ann/som/som1.html
  
  
  
• Krichmar's lab website http://www.socsci.uci.edu/~jkrichma/
  
  

This is a great list so far, and I'd love to see it added to the sidebar.

I'd suggest adding "Neuroethics" by Martha J Farah under "Other". It gives an interesting perspective on the influence of neuroscience on law and society.

http://www.amazon.com/Neuroethics-Introduction-Readings-Basic-Bioethics/dp/0262514605

"The Human Brain in Photographs and Diagrams" is good for anyone interested in neuroanatomy. I've only used the 3rd edition- there is an updated edition, but I can't speak to how useful it is.

http://www.amazon.com/Human-Brain-Photographs-Diagrams-CD-ROM/dp/0323045731/ref=sr_1_2?s=books&ie=UTF8&qid=1411223019&sr=1-2&keywords=the+human+brain+in+photographs+and+diagrams

"Structure of the Human Brain" is a very comprehensive section-by-section atlas of the brain.

http://www.amazon.com/Structure-Human-Brain-Photographic-Atlas/dp/019504357X/ref=sr_1_1?ie=UTF8&qid=1411223434&sr=8-1&keywords=structure+of+the+human+brain+a+photographic+atlas

"Molecular Neuropharmacology" is a good advanced text for anyone interested in drug development.

http://www.amazon.com/Molecular-Neuropharmacology-Foundation-Clinical-Neuroscience/dp/0071481273/ref=sr_1_1?ie=UTF8&qid=1411223180&sr=8-1&keywords=molecular+neuropharmacology

I've mentioned these because they seem to fill gaps that are currently in the library. If anybody knows of better anatomical texts, though, I'd be interested to know about them!

I used these two books to prepare:

• Comprehensive Review for Stroke Nursing is published by AANN and is supposed to cover all of the content that is found in the SCRN exam. The drawback is that it's in outline format, so it's a little more difficult to follow than your typical textbook. A couple of the sections didn't seem fully fleshed out either, especially the chapter on meds. And there aren't any pictures or diagrams, so following the section on anatomy would have been difficult without a different reference to compare. Speaking of which...
  
  
• Clinical Practice of Neurological and Neurosurgical Nursing is a great textbook that covers a lot more than just strokes, but the relevant chapters work well as a companion to fill in the blanks in the Comprehensive Review. My guess is you could probably get away with just studying this textbook, but make sure you check out the test content outline in the candidate handbook to make sure you're not missing anything.
  
  They are a little expensive, especially the Review, so hopefully you can find someone to borrow these from, or check with your unit educator or hospital library to see if they have copies.
  
  Good luck! I found the test itself fairly challenging, but I was able to pass the first time through. The hardest parts for me were the questions about ER and critical care since I work on a med/surg-level neuro floor.

You are going to get a lot of recommendations for 'mainstream' neuroscience books, which is not a bad thing, but it might be useful/fun to get an alternative perspective as well. Something like The Embodied Mind by Francisco Varela, Evan Thompson, and Eleanor Rosch might be nice. Also a 'networks/graph theory' approach would be a great perspective to get. Networks of the Brain by Olaf Sporns is a great resource, and these approaches are on the upswing IMO.

The hand book of NSG is a must

Neurocritical Care is a must if you have a MICU/SICU

Neuroanatomy Through Clinical Cases is a good textbook which focuses on all the major points and many fine details you will need to know as you go through your career. I use this book when I make lectures.

You'll also want to read some review articles on ICP management, vasospasm dx and tx following SAH, hypertonic saline, neuro imaging.

There are some youtube videos that will help get you started with imaging:

For Head CT

For C-spine CT

For MRI in general

For Lumbar MRI

I recommend referring back to these resources as you see patients with the afflictions as it will help it stick. If you just read about things without using them in practice I think you'll find it is easily forgotten. Best of luck!

"What are the parts of the brain and what do they do?" is a much better formed question, but now you're getting into unsolved questions and areas of active research. There are hundreds of "identifiable parts" of the brain, and short of writing an entire textbook, there's not much I can do to answer such a broad question!

My recommendation for you would be to keep reading wikipedia (maybe start here ) and if you run into any specific questions come back to reddit and ask them, or try to get your hands on a basic neuroscience text book (the Purves book is good).

I hope I don't sound too discouraging! If you have specific questions I'm happy to answer them and I'm sure other panelists are too, but for me personally, I don't want to spend more than an hour answering any single question, and as it stands, your question would take me hours to answer well.

You are asking about a very broad topic. Books are written about the role of dopamine in the brain.

For the specific case of ADHD, methylphenidate (Ritalin) acts to increase dopamine and norepinephrine by blocking reuptake - meaning it does not work by binding directly to dopamine receptors. Because of NE, its actions are predominantly as a sympathetic stimulant rather than a dopaminergic agonist.

For schizophrenia, you can see a reduction in positive symptoms (delusions, hallucinations, loose associations) with typical antipsychotics that are thought to act as antagonists at the D2 receptor. We don't have any drugs that significantly improve negative symptoms (dissociation, flat affect, etc). This receptor is also though to mediate the extrapyramidal side effects - including disorders of movement. If you've ever seen a patient on these drugs, you'd know what I mean about sedating - sometimes they can barely keep their eyes open.

The newer atypical antipsychotics seem to work more through serotonergic (5-HT2) receptors, with a great example being clozapine, with almost no activity at the D2 receptor, but strong activity at 5-HT2 and mild affinity for cholinergic receptors. Consequently, clozapine has minimal extrapyramidal symptoms and may even improve tardive diskinesia. Of course, with these drugs, you have significant weight gain as a side effect.

So, suffice it to say, the role of dopamine in normal functioning, disease, and treatment is complicated and can't be easily summed up in a reddit post - and certainly not as in the top post above.

If you want the standard sequence of Neuroscience textbooks, there is a rough ordering of 3 common books. Each are very comprehensive and more than you would likely be able to read cover to cover, but they get more sophisticated and comprehensive as you go. The last one specifically is essentially the bible of neuroscience and you will be hard pressed to find a more comprehensive coverage of any of the topics outside a specialised textbooks or research papers.

These books will cover the general overview of neuroanatomy, physiology, pharmacology and pathology but if you want to go further in depth, there are more advanced books for each of those and dozens of other subfields.

1. Purves - Neuroscience
   
2. Bear - Neuroscience: Exploring the Brain
   
3. Kandel - Principles of Neural Science
   
   I would specifically recommend Nolte - The human brain: an introduction to its functional anatomy as an exceptional example of a specialised text. Unfortunately, I do not recall the neurpharmacology text that I used, but it was very good too. I shall look it up and get back to you! For a more general introduction to pharmacology, the standard text is Rand and Dale - Pharmacology.

I'm not sure about single-cell recordings but with EEG experiments (and most other electrophysiological measures i.e. EOG, EMG) the voltage (also known as electrical potential difference) recorded at a place on the scalp is measure of the potential for current to move from one place to another. So you need 2 electrodes to measure this: the one (or more) on the scalp, and the ground electrode which provides a common reference point for all the other electrodes.
As far as I recall the site for this can vary, I know the EEG system we use (Biosemi) has two electrodes that work as grounds that are placed on the scalp (they are slightly more complex than just ground electrodes though but don't ask me to explain how!).

In addition (and slightly confusingly) you have reference electrodes, which can be placed in a variety of places (earlobes, nose, mastoids, etc for EEG). The key property of a site for a reference electrode is that it must be unaffected by the source you are recording. It picks up all the internal and external noise and is then subtracted from the active electrodes to give a cleaner signal. Just like the normal electrodes the reference is measuring the potential difference between itself and the ground electrode.


Luck (2005) puts it like this: Signal = AG voltage - RG voltage

[A = Scalp electrode, R = Reference, & G = Ground electrode]


Hopefully that makes sense and feel free to correct me if I'm wrong!

Source: I'm Cognitive Neuroscience PhD student, & Luck (2005) explains this pretty well.

For the type of graph (network) theory that is currently hot in neuroscience contexts, [Newman's book](http://www.amazon.com/Networks-An-Introduction-Mark-Newman/dp/0199206651
) is a great compendium (quite readable, but fairly comprehensive).

For bedside reading about mammalian cortical networks in particular, Networks of the Brain and Discovering the Human Connectome, both by Olaf Sporns, are well worth a look.

From there... it's already becoming a pretty big literature. If you have some specific areas of interest, I can do my best to point you to resources. Take my suggestions with a grain of salt, though... I'm a pure mathematician who kinda got seduced into applied maths... which means I probably don't know as much about either discipline as I should.

Hi there,

Some suggestions for ya!

The Quest for Consciousness by Christof Koch. Minimal neuroscience background required, but the more you know, the more you'll derive from this book. Focused on illustrating how complex networks can manifest behaviour (and consciousness). Outside of Koch's regular pursuits as an electrophysiology, he worked alongside Francis Crick (ya that one), to study arousal and consciousness. It's a fantastic read, and it's quite humbling.

Rhythms of the Brain by Gyorgy Buzsaki. Written for neuroscientists and engineers as an introductory textbook into network dynamics, oscillations, and behaviour. One of my favorite books in the field, but it can also be the most challenging.

Treatise of Man by Rene Descarte. Personal favorite, simply because it highlights how far we've come (e.g. pineal gland, pain, and animal spirits).

Synaptic Self by Joseph LeDoux provides the fantastic realization that "you are your synapse". Great circuit/network book written with a lot of psychological and philosophical considerations.

Finally...

Physical control of the mind--towards of psychocivilized society by the one and only Jose Delgado. (In)Famous for his experiments where he stopped a bull charging at him through amygdala stimulation- along with some similar experiments in people- Delgado skirts the line between good intention and mad science. It's too bad he's not taught more in history of neuroscience.

Let me explain why I brought up dyslexia as a common comorbidity of having problems expressing yourself and adhd, but first lets talk about language. I will get back to dyslexia and ADHD. Do note while my post is long, I provide lots of links to pictures.

I am going to be using a lot of images from a biology textbook called Biological Psychology: An Introduction to Behavorial, Cognitive, and Clinical Neuroscience. Mostly from Chapter 19 which deals with language, while I am going to provide specific images you may find it useful to read the visual summary if you want more info.

http://7e.biopsychology.com/vs19.html

-----

 

 

Put simply to do language you are going to use multiple regions of the brain together as a circuit. See here

http://7e.biopsychology.com/vs/vs19/vs1905.png

You are going to use areas in the back of the brain tied to vision, then you are going to pass that information to a multisensory processing area where your brain combines the senses and figures out what to do (aka you are forming the visual images in your mind before you think of the words that correspond to the visual images). You are then going to pass the information once again to a multisensory processing area but this area is more auditory based, followed by you passing the information to a specific area of the frontal lobe that is very close to the prefrontal areas which is tied to language, but also attention, sequencing of data, and response inhibition (stopping impulsivity) but also activation (aka release the brake and now go). This information is then passed to premotor and supplementary motor areas which is then passed to the motor areas. And during all these steps there are inbetween fine tunning by the subcortical brain areas such as the cerebellum and the basal ganglia.

Now I was trying to explain all of that without using medical terms but here is the names for those brain areas

http://7e.biopsychology.com/vs/vs19/lowres/BIOPSYCHOLOGY7e-Fig-19-07-0.jpg


 

 

And here is a diagram that compares speaking a heard word and speaking a word you read off a piece of paper. When you are composing inside of your head without mental feedback and you are imaging what you are going to say your thought process looks more like speaking a word you read off a piece of paper for you use more of the visual areas to visualize in your mind's eye what you are going to do and say.

http://7e.biopsychology.com/vs/vs19/lowres/BIOPSYCHOLOGY7e-Fig-19-09-0.jpg


 

 

-------

Now we know things like head injuries and lesions to specific brain injuries to specific brain injuries can all disrupt speech but if the area is localized to specific regions you may only have some problems with certain aspects of language. When language problems are caused by some form of trauma we call this aphasia.

http://7e.biopsychology.com/vs/vs19/artWin.html?BIOPSYCHOLOGY7e-Table-19-01-0.jpg


 

 

And people with different types of aphasia may have different problems. Like a person with expressive aphasia may know what they want to say and they can draw what they want to say but they can't find the words for it. While people with receptive aphasia have problems understanding language. Now receptive aphasia can be more than this where people accidentally skip words in their explanations that are crucial in the sentence, or they have anomia where they know what they want to say (the word is on the tip of their tongue) but they can't remember it, or they do an unintentional word subsitution subsituting another word with a similar sound or meaning, sometimes they mess up not the grammar of the sentence but the word tense, or use the wrong pronoun (like her vs she)

• A subtype of this with additional issues with the left and right half of the back of the brain not talking as well as they should is Dysprosody sometimes called foreign accent syndrome for you do not talk with the local accent / family accent. People with dysprosody have problems with the timing of sounds and things like rhythm, cadence, pitch, and movement of words. They can't tell when you are inflecting or not. This is quite important for they do not get a lot of important information in communication such as emotional tone and inflection which can rapidly changing the meaning of something. Most humans are annoyed by synthetic computer speak for it just sounds wrong, now imagine if everyone spoke like that and you were not familiar with what most of us would consider normal speaking.
  
   
  
   
  
  Now all of these issues I described were studied in people with head injuries. That said we see much the same pattern of behavior with many different types of disorders, one of which is autism, but another of which and is completely separate is dyslexia.
  
  Now with dyslexia many brain regions are implicated and some of them are the same areas I have shown above
  
  http://7e.biopsychology.com/vs19.html (go to slide 6)
  
  In many forms of dyslexia you are not using the back of the brain areas tied with the early visual information which is passed to the angular gyrus which is passed to the wernicke area. See picture
  
  http://www.hoperesourcecentre.com/wp-content/uploads/Brain-Illustration-CellfieldCanada.jpg
  
  And you are trying to compensate for all of this information with actually using more of the frontal lobe to compensate for these areas. Well the frontal lobe is not designed to do such a thing its arrangement and types of nerve cells are different.
  
   
  
   
  
  Now its not just that picture I showed you, its also some of the subcortical areas such as these areas I am about to post here
  
  http://7e.biopsychology.com/vs/vs18/artWin.html?BIOPSYCHOLOGY7e-Fig-18-15-0.jpg
  
  Involving the thalamus and an area known as the pulvinar, as well as certain areas of the brainstem, and certain areas of the cerebellum mainly vermis 6 and vermis 7 (often labeled VI and VII)
  
  http://www.frontiersin.org/files/Articles/156522/fnins-09-00296-HTML/image_m/fnins-09-00296-g002.jpg
  
  These parts of the cerebellum are used for multiple functions but they are often called the occular motor areas of the cerebellum. They are also involved with the control of attention and shifting smoothing from one object to another for one of the purposes of the cerebellum is to "fill in the blanks" between gaps. Imagine you were watch a film but instead of watching a video you were seeing slide by slide, well the cerebellum along with the thalamus and brain stem regions are used in the predicition of what is going to happen next and smooth movements of the eyes, while other areas in the frontal lobe are more involved with figuring out these things are important so why don't we set this as the new priority of what to look at and the rest of the brain figures out how best to move there.
  
  https://kin450-neurophysiology.wikispaces.com/file/view/SACCCAAADDDEEESSS.jpeg/393831860/480x346/SACCCAAADDDEEESSS.jpeg
  
   
  
   
  
  Now if you have not probably figured out there is a connection to all of these brain regions with ADHD. Some ADHD people have these issues, but if you have these issues you are also more likely to have ADHD.
  
  If you look at the previous chapter 18 of Biological Psychology you will see this picture on slide 6
  
  http://7e.biopsychology.com/vs/vs18/artWin.html?BIOPSYCHOLOGY7e-Fig-18-16-0.jpg
  
  There are two attention networks here. The top attention network is known as the frontal parietal control network where it controls and and it also modulates the dorsal attention/perception network. While a second bottom network in orange involves the frontal lobe and connects to areas shared both with the temporal lobe and the parietal lobe where they meet and the surrounding areas, this bottom attention network is more with detecting new things and novel things, while the top network keeps you on track and looks for the goals held within working memory to solve the problems.
  
  If you have not noticed the same areas of the brain that make it hard to express onceself with language, are also the same areas that are common in dyslexia, and are the intersection of two of key networks tied with attention (now there are more than those two networks I just showed you with ADHD but now you understand why there is a connection.)
  
  (Now most of pictures I linked to came from Biological Psychology by Breedlove and Watson, this is an introductory college text meant for undergraduate use. It will not go into all the stuff involving the brain with attention and such, other books made by the same publishing company (Sinaeur) but done by other authors are better if you are mainly wanting to talk about attention instead of language such as
  
  Sensation and Perception
  
  Neuroanatomy through Clinical Cases
  
  Dale Purves Neuroscience 5th Edition
  
  And Principles of Cognitive Neuroscience

Lazy citations:

• Anderson (2017) [PDF]: Of Bayes and bullets: an embodied, situated, targeting-based account of predictive processing
  
• Bastos et al. (2012): Canonical microcircuits for predictive coding
  
• Clark (2013): Whatever next? Predictive brains, situated agents, and the future of cognitive science
  
• Clark (2016): Surfing uncertainty: prediction, action, and the embodied mind
  
• Friston (2002): Functional integration and inference in the brain
  
• Friston (2003): Learning and inference in the brain
  
• Friston (2005): A theory of cortical responses
  
• Friston (2008): Hierarchical models in the brain
  
• Friston (2010): The free energy principle: a unified brain theory?
  
• Friston (2011): Functional and effective connectivity: a review
  
• Friston et al. (2018): The graphical brain: belief propagation and active inference
  
• Hohwy (2007a): Functional integration and the mind
  
• Hohwy (2007b) [PDF]: The sense of self in the phenomenology of agency and perception
  
• Hohwy et al. (2008): Predictive coding explains binocular rivalry: an epistemological review
  
• Hohwy (2012): Attention and conscious perception in the hypothesis testing brain
  
• Hohwy (2013): The predictive mind
  
• Howhy (2015): The neural organ explains the mind
  
• Howhy (2016): The self-evidencing brain
  
• Kirchoff et al. (2018): The Markov blankets of life: autonomy, active inference, and the free energy principle
  
• Knill and Pouget (2004): The Bayesian brain: the role of uncertainty in neural coding and computation
  
• Parr and Friston (2018): The anatomy of inference: generative models and brain structure
  
• Ramstead et al. (2018): Answering Schrodinger's question: a free-energy formulation
  
• Seth (2013): Interoceptive inference, emotion, and the embodied self
  
• Seth (2015) [PDF]: The cybernetic Bayesian brain
  
• Seth and Friston (2016): Active interoceptive inference and the emotional brain
  
• Seth et al. (2011): An interoceptive predictive coding model of conscious presence
  
• Tenenbaum et al. (2011): How to grow a mind: statistics, structure, and abstraction.
  
• Wiese and Metzinger (2017) [PDF]: Vanilla PP for philosophers: a primer on predictive processing

If you want to spend some money, https://www.amazon.com/Neuroscience-Fifth-Dale-Purves/dp/0878936955/ref=mt_hardcover?_encoding=UTF8&me= is a perfect resource. This was our textbook for neurobio in college and I still find myself referencing it. It's possible you could find the PDF somewhere online, I haven't looked. If you have a little background in cell bio, this would be a great place to start learning.

Beyond that, I agree with the suggestions of /u/Hypercuboid and /u/Noobsessed.

If you are getting information off of forums (about pathways, interactions, etc.), make sure you do your own research, ask for or check sources, and try to understand the mechanisms. Wikipedia rabbit holes can sometimes help with this in the beginning, and can provide you with reliable sources/resources to follow up on. Keep in mind that the answers are almost always not as simple as people portray them. Many MOAs are not fully understood, especially with nootropics. That's why some refer to these substances as RCs and us as guinea pigs; because, in large part, that is true.

Goodwin and Guze psychiatric diagnosis (https://www.amazon.com/Goodwin-Guzes-Psychiatric-Diagnosis-Carol/dp/0195144295/ref=sr_1_1?ie=UTF8&qid=1491703771&sr=8-1&keywords=goodwin+and+guze+psychiatric+diagnosis)

I'm a psychiatry intern, and this is the book I read every night. Very will written and both easy and enjoyable to read. It really helps conceptualize the psychiatric assessment.

Psychiatry and Clinical Neuroscience https://www.amazon.com/Psychiatry-Clinical-Neuroscience-Charles-Zorumski/dp/0199360561/ref=sr_1_2?ie=UTF8&qid=1491704272&sr=8-2&keywords=clinical+neuroscience+psychiatry

This is what it sounds like, helps you remember that psychiatry still has roots in neurology.

Personally I love Stahl's pharm book. It has pictures, it's concise, and it's mostly right. If you get to the point where it's not answering your question you're probably past textbooks anyway and need to hop on PubMed.

Neurobiology with Dr. Xu-Friedman was probably my favorite class, and he is probably my favorite professor. It can be a challenging class, but it's worth it if you enjoy neuroscience.

When I took it, we used Purves - Neuroscience 5th Edition .

The book is definitely needed, as it really helps to reinforce what they lecture on. If you're looking to get ahead before the semester begins, I think he follows the first few chapters tightly. If I recall correctly, he starts with some basic neuroanatomy, and then jumps into electrophysiology (similar to what you did in Bio 213 physiology lab, except in more detail) and receptor kinetics.

If Dr. Medler is also teaching it, she can be somewhat abrasive and difficult, but you can still do well if you go to lecture and pay attention.

• Purves text isn't that easy but a great and thorough introduction.
  
• Gazzaniga's text is fantastic but less on the biology side of things.
  
• Others have mentioned Kandel's text but I don't think that's a good first text for anyone wanting to "dip their toes" in.
  
• Someone else also mentioned the Bear text, which is very good.
  
  Those are really all you'll need; from there you will find things on your own or from professors.

No formal education but I have some friends in the field and I've been studying some of their textbooks and talking to them about it a lot lately. Molecular Neuropharmacology is a great book, not an easy read, but full of information. NMDA antagonists like magnesium are also supposed to help prevent neurotoxicity from Ca influx, many people take them for tolerance prevention:

http://www.bluelight.ru/vb/threads/501875-NMDA-antagonists-for-tolerance-a-collection-of-the-evidence-and-anecdotal-reports?highlight=amphetamine+tolerance+nmda

http://www.bluelight.ru/vb/threads/97021-Speed-Meth-tolerance-prevention-reduction-long

I cannot more strongly recommend Steven W. Smith's The Scientist and Engineer's Guide to Digital Signal Processing.

Every chapter is freely available as a PDF on the website.

Everyone who is interested in EEG/ECoG/LFP/single-unit research should absolutely read this book.

Steve Luck's Introduction to the Event Related Potential Technique is great, and will really help you to set up a top-quality ERP lab, but the DSP book really teaches you what happens to the signals you're recording.

If you want to get deeper into the underlying physiology of EEG, I'd recommend Electric Fields of the Brain by Nunez & Srinivasan.

You'd also be happy reading Buzsaki's Rhythms of the Brain.

"Neurology" is a medical discipline, i.e. a department you would find in a hospital, so that would not be appropriate for your case. "Neuroscience", on the other hand, is the name of the scientific study, which is what you should look into, but overall that is too broad of a field.

For what you're trying to learn, I'd just start directly diving into neuropharmacology. Any good resource should give you enough of an overview before things get more complicated; perhaps having a review of high school science might be good, but not necessary.

Here's a recommendation for a good reference textbook: https://www.amazon.com/Molecular-Neuropharmacology-Foundation-Clinical-Neuroscience/dp/0071481273

I agree completely, Kandel's book is definitely my favorite neuroscience text. For a more undergraduate level introduction Neuroscience: Exploring the Brain by Mark Bear et al. and Neuroscience by Dale Purves et al. are good starting places.

I second the Human Brain Coloring Book.
Clinical Neuroanatomy Made Ridiculously Simple is a really great add-on
http://www.amazon.com/Clinical-Neuroanatomy-Book-Ridiculously-Simple/dp/0940780925
Not just for clinical - pathways are better presented here than in other texts.

Digital Anatomist and Dartmouth's Brain slices are great.

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.

Methodology-wise, Steve Luck has a really nice, clear introductory text to ERP techniques.

All right. I took that one on the side while working on my thesis. I'd say the tricky part is SPM; learning what to do, and when to do it. As I mentioned my data were already pre-processed yet I still struggled to understand what I actually had to do in SPM. It's a steep learning curve (at least it was for me) but I was doing it all via GUI since I'm wasn't that familiar with MATLAB prior to this. If you are, then scripting will make your days a lot easier.

This book is a good introduction to the underlying methods of fMRI, if you haven't already got that covered. Then I'd suggest you head over to the SPM8 website and try out their data sets and tutorials!

Oh looks like you may be interested in studying complex systems. http://en.wikipedia.org/wiki/Complex_system

Here's some stuff on using complex systems analysis to look at the brain: http://vimeo.com/13953303
http://www.amazon.com/Networks-Brain-Olaf-Sporns/dp/0262014696

>I mean the concept that Life is a force of the Universe present since its absolute beginning with a function of building toward higher complexity as an opposing force to Entropy, which builds toward nothingness. That the experience of consciousness as we know it is the result of organic matter reaching a critical threshold of complex structure in our brains.

Some people have been working on quantifying consciousness, and they're doing it by measuring reduction in entropy! Maybe you'd be interested in that? Check out Integrated Information Theory. It kinda requires some understanding of Shannon information theory http://en.wikipedia.org/wiki/Integrated_Information_Theory

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

I recommend this book on this topic. It describes wonderfully how different systems of our brain perform Bayesian probability theory calculations, and communicate with each other using some analog kind of error-correcting codes.

Recently read Other Minds, which is about octopus's intelligence. Really good book.

https://www.amazon.co.uk/Other-Minds-Octopus-Evolution-Intelligent/dp/0008226296/ref=mp_s_a_1_1?ie=UTF8&qid=1536053152&sr=8-1&pi=AC_SX236_SY340_FMwebp_QL65&keywords=other+minds&dpPl=1&dpID=51NJolDQXxL&ref=plSrch

EEG analysis is a bit of an art form and mastering it just comes with experience, trial and error, and really knowing your particular dataset and aims. I use Matlab with the EEGLAB toolbox for ERPs and FieldTrip for time frequency analysis.

There are so many different steps, it's definitely not just a matter of pushing a button and getting a nice p-value out at the other side. I'd recommend getting your hands on this book in the first instance.

Former human neuroimager here. This is the best textbook intro to functional neuroimaging.

Dear Octopus, I promise I won't eat your fellow cephalopods anymore.

Will never forget this book/trip.

https://www.amazon.com/dp/0008226296/ref=cm_sw_em_r_mt_dp_U_6kFtDbVWGH00S

Did you know Amazon will donate a portion of every purchase if you shop by going to smile.amazon.com instead? Over $50,000,000 has been raised for charity - all you need to do is change the URL!

Here are your smile-ified links:

https://smile.amazon.com/Fast-Facts-Stroke-Care-Nursing-ebook/dp/B00KAZVPZ4

---

^^i'm ^^a ^^friendly bot

Neuroanatomy made ridicuously simple has a good following but I haven't had neuroanatomy yet so I'm not sure what is good or not.

also http://www.amazon.com/Neuroscience-Fourth-Edition-Dale-Purves/dp/0878936971 is a link to the textbook used. I will not post a link to a pdf of that version (which i cannot actually find) but there is a third edition pdf readily accessible that looks like a different chapter order.

I think the modern classic on ERPs is considered to be Steve Luck's book. I can vouch that it's an excellent book.

I can't help you with non-event-related EEG, though.

+1 on the Purves text. I find it pretty accessible even though my background is not neuroscience (or any other type of biological science). Also, you can pick up a used copy of an older edition for less than $20

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

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

Yes, absolutely. http://www.amazon.com/Introduction-Event-Related-Potential-Technique-Neuroscience/dp/0262621967

OP this is what you want I promise you.

http://www.amazon.com/Molecular-Neuropharmacology-Foundation-Clinical-Neuroscience/dp/0071481273?tag=coursera-course83-20

we use this book in my neurobiology class and i like it.. covers a lot on how neurons do their thing

My school typically recommends Bear's textbook for systems-level information and Purves' Neuroscience for cellular stuff. I prefer Purves because it actually covers both subjects and it goes more in-depth on the molecular topics!

I am preparing to take this exam. People have recommended the Strokes FAST Facts https://www.amazon.com/Fast-Facts-Stroke-Care-Nursing-ebook/dp/B00KAZVPZ4

Also this:
Stephen Luck's book

I also work on a neuro unit (neur & tele) and am a new grad. I was looking at getting this book...

Clinical Practice of Neurological & Neurosurgical Nursing https://www.amazon.com/dp/1451172672/ref=cm_sw_r_cp_api_aIKvyb6YZDP5G

Here is the mobile version of your link

kandel
bear
purves
martin

Each of these books is aimed at a different audience, but this should get you started.

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.

That would be highly unusual and unlikely. I won't say it's literally impossible, but there's absolutely no reason to make such an untenable assumption when a clear and solid motivating factor, such as in this case, the separation from a close partner, exists.

"LSD, which is widely abused, does not appear to be addictive." -source

"In contrast to many other abused drugs, hallucinogens
do not engender drug dependence or addiction and are
not considered to be reinforcing substances (O’Brien,
2001)." -source

"There are no literature reports of successful attempts to
train animals to self-administer classical hallucinogens, an
animal model predictive of abuse liability, indicating that
these substances do not possess the necessary pharmacology to either initiate or maintain dependence. Hoffmeister
(1975) has reported that LSD actually had negative
reinforcing properties in rhesus monkeys trained in an
avoidance task." -source

"Several other classes of drugs are categorized as drugs of abuse but rarely produce compulsive use. These include psychedelic agents, such as lysergic acid diethylamide (LSD)" -source