(Part 2) Best products from r/neuroscience

There's a very big conversation to be had here, but I'll take a shot at giving the short version:

My Ph.D. is in Educational Psychology/Technology, and my areas of focus are game-based learning and situated cognition. Virtual reality isn't really new in education--people have been talking about it since the '60s/'70s and even farther back if you count Dewey (he was big on the whole authentic, inquiry-based learning thing). There are still some issues to be worked out with the tech itself (like the motion sickness it causes), but that's not really a cognitive issue. What learning scientists wonder is: will real-to-life simulations/VR scenarios (a la the Holodeck) actually make learning/instruction better?

The answer is:

...maybe.

There's no doubt (at least as far as a situated cognitivist would judge) that richly authentic problem-based learning (PBL) environments are the best way to induce and assess learning. I can't speak to the neurochemistry underlying it, but (as mentioned below) transfer isn't likely unless you have optimal generator sets capable of inducing it. Ideally, knowledge is demonstrated, not just spoken--after all, you can tell me you know how to bake a cake or change your car's oil, but unless I see you do it, I can't confirm learning has occurred. In situated cognition, knowledge and doing are inseparable). Put another way: the world isn't in your head; your head is in the world--that is, interaction between your physical body and the environment is what constitutes knowing.

So what does this have to do with VR?

Well, what we know from about a decade of game-based learning research is that simulations are pretty good at helping people learn to complete particular tasks because they're designed to be as isomorphic with the real world as possible, but the jury is still out on games (that's another conversation I'll try to avoid here). What really matters is how those simulations are used instructionally. Back in the 1960s, Papert tried to push PBL with something called Logo. It was a programming language accompanied by a little robotic turtle that drew shapes on the floor based on input from elementary school students. It worked really, really well as a way to teach geometry and early programming because it situated the learning in a context where it was being directly applied. The teacher's job was not to provide direct instruction about how to use the tool but to facilitate exploration, inquiry, and critical thinking. Unfortunately, the program floundered because schools--being what they are--stopped teaching WITH Logo and started teaching ABOUT it. Students would be given worksheets telling them to input specific commands, and the inquiry piece would be lost. Without inquiry, it became another skill n' drill activity that was poorly situated and had almost no overlap with real world action. The whole point of education is to draw attention to invariance between contexts in order to help students problem solve. That stopped happening as teachers talked and tested rather than using Logo as a way to spark conversations about real world application.

The same issue arose with Apple's Hypercard and the Cognition and Technology Group at Vanderbilt's Adventures of Jasper Woodbury laserdisc series. Education was the Borg: it gobbled up initially good ideas and turned them into direct instruction. Simulation activities are subject to the same risk.

But let's assume that won't happen this time. Teachers and administrators will get VR, and it will totally change our pedagogical approach to public education! Then what?

This is a snippet from something my colleagues and I wrote last spring (we were talking about narrative, but the point still stands even if you replace narrative with VR):

"We also feel it necessary to acknowledge how approaching this topic leaves us vulnerable to oversimplified questions about the instructional value of both games and narrative. Assuming the effects of narrative on curricular achievement are situated (i.e., contextualized in the rich, complex human-to-human, human-to-computer, and human-to-social environment interactions that are simultaneously present when students play video games in schools), a particular game’s narrative can be simultaneously optimal and non-optimal depending on the context in which it is used, by whom, and toward what goals. Some narratives may prove successful for some students with shared goals and intentions in some curricular content areas (e.g., CTGV’s Jasper videos for teaching distance-rate-time relationships), but even widely valued narratives will inevitably fail to serve learners with all imaginable goals for playing, educator-created student objectives, and instructional needs, even if those learners, educators, instructional needs, and contexts are mostly similar. A rich, text-based roleplaying narrative might lead to successful dialogue about abstract concepts like human cognition, bioethics, or intercultural competency, but the same structure could collapse in a content area based on molecular action (e.g., the chemical processes responsible for muscle fiber movement) or pharmacological drug interactions (e.g., in vivo enzyme and substrate bonding/cleaving). Conversely, a narrative that relies on a visually detailed digital environment (e.g., the realistic interaction of two subatomic particles) might be too mechanically restrictive and graphic-dependent for learners to explore and discuss social, political, or historical concepts (e.g., the cultural and social conflicts that led to the American Civil War)."

The summary is that, again, whether or not a particular approach to instruction will work depends on context.

My best guess based on our findings from work we published back on 2012 (and the meta-analyses that accompanied it--e.g., Wouters et al (2013), Tobias and Fletcher (2011)) is that VR would probably be helpful for trade schools, medical training, engineering, and the sciences but probably not a big deal for the humanities. Sure, you might be able to simulate Edgar Allen Poe reading you The Telltale Heart, but unless the VR experience can also capture the events/culture/society unfolding around the situation in question, the content isn't going to seem terribly useful outside the context in which it's taught (this is the reason why playing a game like Math Blaster doesn't help you make change at the grocery store--transfer is incredibly hard to detect, and it relies on the induction of particular goals, tuning of perception, and more.

So there you have it--virtual reality tools like Oculus Rift are really neat, and they might help us train people in the future the same way we currently use flight simulators train pilots and operating room simulators train surgeons. However, it seems likely that if they catch on in K-12, they won't revolutionize education. Instead, they'll become another innovation that's either crippled by implementation issues or really not all that much better at improving instruction than using alternative, already-existing tools (kind of like what's happened with interactive whiteboards). Tech for tech's sake isn't usually a good thing. What really matters is the instruction accompanying the tech. The better teachers are at developing a foundation for students to see similarities/differences, critically think, and problem solve, the more useful VR will be for programs where direct action would be too expensive, dangerous, or otherwise inconvenient (e.g., laboratory experiments, surgical procedures).

TL;DR: VR is kinda cool, but it's probably not going to change education a whole lot.

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 can't answer the question definitively, but an interesting phenomenon that I've observed as a teacher is how teaching infants sign language allows them to exhibit their cognition and thought process. I teach in a Montessori infant classroom and I've taught babies (under 18 months) signs that I then see them use in different but totally appropriate ways. For example: we use the sign "outside" to mean literally out-of-doors in the fresh air. But we have kids who then use the same sign to mean "out of the classroom", "out of this area", "come to this side of the fence," etc. They are showing that they understand this concept of "i am here and I want to be elsewhere". They don't have the verbal/physical words, but the neural pathways are certainly there, and every time someone uses that sign or says "outside" that pathway is being reinforced. Obviously, I don't know for sure, but I would imagine that since these children are signing in response to heard words, if you could see a brain scan you'd see areas lighting up for thinking of the sound of words, and also motor skills for thinking about the movement of signing.
Now I want to see if anyone's done this kind of study, and if not, why not?! Off I scuttle to do some research....

Edit: a really terrific resource for understanding infant cognition is Alison Gopnik. She's a cogsci researcher out of UC Berkeley and she's written the following:

[The Scientist in the Crib](The Scientist in the Crib: What Early Learning Tells Us About the Mind https://www.amazon.com/dp/0688177883/ref=cm_sw_r_cp_api_bboSybE1V7Q9G)

[The Philosophical Baby](The Philosophical Baby: What Children's Minds Tell Us About Truth, Love, and the Meaning of Life https://www.amazon.com/dp/0312429843/ref=cm_sw_r_cp_api_QboSybDSGGJZQ)

I can't speak for [her new book](The Gardener and the Carpenter: What the New Science of Child Development Tells Us About the Relationship Between Parents and Children https://www.amazon.com/dp/0374229708/ref=cm_sw_r_cp_api_udoSybM1SFBSC) but I can tell you that the first two completely changed the way I view babies. They really are amazing little people with astounding cognitive abilities from birth!

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.

I read this book and it taught me a lot about the basics of intelligence. Essentially, longitudinal studies have demonstrated that IQ is stable throughout one's lifetime.

Here's how I think about it. Intelligence, as indicated by g, represents a set possibility for how deep you can go into complex topics. This does not account for other personality details, or enjoyment of a topic, which I think can matter tremendously in what you end up doing with the skills and knowledge you obtain.

The intelligence you have is likely to be set at whatever point it's at now. But it seems like you care about improving your mind and are curious about the world, and honestly I think that will lead you to things which improve your reasoning over time. My only advice is to be kind to yourself. I have to remind myself to do that daily.

Hobson is definitely the guy I would recommend, I would go with his popular press book (which has a fair bit of jargon) but is a really good and interesting read that should be understandable if you're willing to do a bit of googling (mostly just understanding neurotransmitters and the names of the brain regions). The book is "The Dream Drug Store". It goes into a lot of the neurobiology of it while also attempting to understand how that relates to normal cognition and ultimately attempting to link levels or degree's of consciousness and altered states of consciousness (from drug use) to dream states, it's actually really cool and pretty well written.

I actually want to say to NOT go into overdrive with reading up on material before school starts. Because scientific research is so diverse, two people can say different things about the same subject and both be right (or at the very least, neither will be wrong). This may confuse you down the line or trick you into complacency (oh read about this already so i wont need to focus on it as much). At my school at least, most of our curriculum was based off research that our own professors did or were part of, so the details may be specific to their research.

That said, it wouldn't be a bad idea to read up on electrophysiology because it may be one of the more complicated subjects. Solid fundamentals are important for later stuff like CPGs (central pattern generators) or whatever. Dont have a particular book in mind but the one we used was Cellular Physiology of Nerve and Muscle by Gary Matthews. Neuroanatomy is another big subject but I dont know if it's something I'd recommend trying to read up on your own at first

Also, if you're interested in neural engineering, skills in computer science are HIGHLY useful. I'm doing research now in brain injury and a lot of our research involves programming/engineering. I've probably applied 10% of my neuroscience knowledge, everything else has been computer related. The extent of my comp sci education was C++ in high school, so I'm fortunate that I was already good with computers. Specifically, I'd look into reading up on how to use MATLAB and if you're really ambitious, take a shot at reading up on digital signal processing. There's a free book here

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.

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.

“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/

You should read Steve Luck's book. It's a relatively gentle introduction to many of the concepts in EEG and electrophysiology more broadly. I haven't read the second edition, but the first edition is excellent and I imagine the second edition being equally so.

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.

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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

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

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.

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

Connectome by Sebastian Seung is good

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

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

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

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?

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.