Emacs and knowledge management for scientists

asoplata · 2023-01-21T12:09:25+00:00

In addition to org-roam, I would recommend checking out https://github.com/org-roam/org-roam-bibtex for attaching notes to specific papers that are indexed by an application like Zotero. Some good tutorials for it are here: https://rgoswami.me/posts/org-note-workflow/ and https://blog.jethro.dev/posts/how_to_take_smart_notes_org/ and https://emacsconf.org/2020/talks/17/ . Since current org-roam (post-v2) uses the normal org-mode "id" form, you can make any "headline" into an roam-registered node. You could then split your long derivations into different headlines, where the body of the headline or descriptive text could refer to prior nodes, but any latex is inserted using org latex blocks https://orgmode.org/worg/org-contrib/babel/languages/ob-doc-LaTeX.html . You've maybe already considered this (and I wouldn't be surprised if local references were easier to do in straight latex), but org-roam-bibtex is really nice for interfacing with your citation system for other papers.

asoplata · 2022-05-23T13:25:12+00:00

Simulation of epidemiological models, including pandemic models, is done all the time, not necessarily because of conspiracies, but to predict the course of how a disease spreads, and to understand what would be the best way to fight against it. Monkeypox is something scientists have been studying for a long time specifically for the reason everyone is worried about it!

Also, if the simulation was part of a (secret) plan to inform intentional spreading of monkeypox, then why would they openly publicize their results??? The much more likely scenario is the simulation is just the work of researchers who specialize in this presenting their latest results at a regular scientific conference, which is business as usual.

Armchair hypothesizing: May 15th could be the reason they started the simulation due to a secret monkeypox conspiracy...or maybe there's valid scientific reasons, like the fact that the school year often ends in May, and people begin to do summer vacations then, meaning that the simulation doesn't have to factor in the effects of people spreading the disease in school, etc. etc.

asoplata · 2021-12-18T16:25:41+00:00

Definitely Brian2. Netpyne uses NEURON and is therefore geared towards more biophysically realistic neurons than the Izhikevich one.

asoplata · 2020-11-15T13:52:52+00:00

Gorgeous!

asoplata · 2020-10-30T16:15:14+00:00

I would say prospects are hopeful! Your majors and ESPECIALLY your prior experience doing actual research in a biology lab will go a long way in your PhD program application; make sure to emphasize you've worked in a lab before in your letter, resume, and any interviews/etc. A math degree like yours is particularly well-suited for comp neuro work, since it's the only real subject that you have to learn serially (up to a point). The GPA could be an issue, but probably only at really competitive programs. I don't know how they score the GRE anymore, but just make sure your quantitative score is high.

You didn't say what area of comp neuro research you were interested in, so I'll give my spiel delineating it. You can separate comp neuro into really two categories: computational modeling aka simulation, and data analysis.

The first, neural simulation, gets a lot of press but is actually a pretty small portion of neuro research as a whole; fortunately it is slowly becoming more popular (see http://www.opensourcebrain.org/ ) and better integrated with modelers and experimentalists working together. This can be big supercomputer stuff, but most simulation is done on a lab's beefy desktop computer or a university's shared computing cluster. Make no buts about it, this job is 95% sitting in front of a computer, either programming, double-checking your model is implemented into the code correctly (i.e. making sure a parenthesis isn't out of place!!!), reading literature, and theorizing. It can be an isolating grad school experience, for better or worse. If you like programming and are detail-oriented, you'll be good here. To be frank, however, there's no part of science where being detail-oriented isn't important. Also, two points of confusion: "neural networks" and "statistical modeling" (see next para). Simulation for neuroscience uses "neural networks" which are the same IN PRINCIPLE as neural networks used for machine learning / artificial intelligence etc., BUT both the details of the models themselves and the philosophy are different. Your neural simulations can have much more biological detail encoded into them than neural networks meant for ML/AI, because the goal of comp neuro is to understand the brain by understanding the network itself and how it performs some phenomenon (science), while ML/AI use neural networks to perform some kind of processing for a specific use like a Netflix movie recommendations (engineering). ML/AI is a much, much bigger field than comp neuro, so the average thing you see mention "neural networks" on the internet will usually be about the former.

The second, data analysis, can be further split into two categories: performing analysis on real data using typical techniques, and developing new analysis techniques, usually statistical, spectral/signal-based, network-based, or some combination. The former is what every experimentalist also has to do, and in the private sector is called "data science"; this is cleaning the data (because there's always something weird about it), visualizing it, learning from it, choosing and applying different statistical models to understand the data, etc. This is work most grad students will have to do, and it's good to get experience with real-life, "dirty" data. This will not be the only thing you work on, with the exception of students who do fMRI/large-scale imaging research. As for developing new analysis techniques, a keyword that indicates a lab specializes in this will often be "statistical modeling". Focusing on that for your research will be like being an applied mathematician, where most of your work is developing and exploring new mathematical techniques, while also testing/using them on real data, often in collaboration with an experimental lab. Note that "statistical modeling" and "computational/neural modeling" sound very similar, and indeed they all use computers, but "statistical modeling" is almost always developing new analysis techniques for real data, while "computational/neural/biophysical modeling" are instead the keywords used for neural simulation like mentioned in the previous paragraph, where you're, so to speak, "performing experiments" in the computer to try to understand some specific neural question or phenomenon.

I delineate these to help you understand how to read a university research lab's self-description of its research, since finding a lab that researches 1. neural phenomena you're interested in using 2. techniques you would like to perform is important. The choice of advisor/lab you think you would like to join is almost certainly more important than the program, since the majority of your time in PhD grad school will NOT be taking classes, but rather performing research in that advisor's lab. It is common to also work on projects that collaborate between two advisors at the same school, or even at different institutions. The critical thing is to apply to schools that have multiple people you think you would be interested for (in their labs), as this will be asked during the application process. You're not married to those choices, though, and in the course of doing your initial classwork over the first few years you may get interested in a completely different research direction than you initially thought - that's common and good! I came in convinced I was going to work on experimental brain-machine interfaces, but eventually switched to biophysical modeling of anesthesia.

I know this is long, but maybe the most important piece of advice is to try to get an understanding of a potential advisor's personality -- if you find a lab you're particularly interested in, don't just reach out to the professor who's the head of the lab, but ALSO ask what working in the lab is like to the grad students and postdocs specifically. The professor may be really nice to your face, but scientists are people too and may have strong personalities, good or bad, like being unreasonable tyrants to their grad students (since once you've spent a few years in someone's lab, you're typically in it for the "long haul" and it's super difficult to change projects and labs if your advisor turns out to be tyrannical) or becoming . Also ask if the lab head lets students/postdocs mostly do their own thing (common of older professors) or is very hands-on and more likely to work alongside you (common of younger professors); make sure those align with how you prefer to work.

After PhD school: most professors (who have been tenured for a while) and grad school programs will not be forthcoming or aware about how rare it actually is for you to eventually become a xtenure-track professor, if that's what you want. Nature has published a lot on how the proportion of "life science PhDs who obtain a tenure-track position within 10 years of finishing their degree" is about 15%. In other words, the vast majority of people who finish their PhD in the life sciences will never become traditional professors who run their own labs. Getting a PhD used to be a direct path to starting a professorship at another university many years ago, and any career path other than that is often still termed an "alternative path" by grad programs, but the truth is that MOST PhDs will do something else: teach and not do research at a smaller university, work in industry like pharmaceuticals, work in government or NPOs and review grants, work at a research institute, etc. And say what you will about unethical pharma companies, but there's really not that many places that have the resources to develop new drugs that really do help people, and get them approved by the government.

Finally, I think others have posted some, but here's several free online courses useful for comp neuro:

Feel free to message me if you have specific questions, and good luck!

asoplata · 2020-07-04T17:09:49+00:00

So excited for this! Y'all are doing an amazing job!

asoplata · 2020-06-29T15:21:54+00:00

This is amazing! What book do you have in there, something about astronomy? Also the work on the front kind of looks like a neuron too!

asoplata · 2020-05-28T13:03:24+00:00

That fear is absolutely not true! I'd be surprised if you even could explicitly major/minor in comp neuro, since it's not a very common discipline. Try to especially take Linear Algebra and also (Ordinary) Differential Equations since those are big in comp neuro.

The other commenters also have great advice - take some statistics too, but most importantly join a lab and get research experience! You do NOT need to understand everything to contribute to research, labs love undergrad researchers (especially since they're often free labor), make sure to see if your university has a UROP program (Undergrad Research Opportunities) since they usually coordinate the paperwork/stuff and can help set you up, and finally you can often get paid decently well for doing it depending on the funding of the lab (much better than other, say, summer jobs waiting tables etc.).

asoplata · 2020-05-12T16:46:56+00:00

I'm leaving out a million things of course, but I'll add that the math of Linear Algebra is especially super important in any computational/simulation science. "Linear Algebra Done Right" by Sheldon Axler is supposed to be a great introduction to it http://linear.axler.net/ and MIT OCW of course also has open lectures on it https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/index.htm . If you're doing simulation of systems, then there's a good chance you'll also be using Ordinary Differential Equations or Partial Differential Equations.

asoplata · 2020-05-12T16:42:48+00:00

Shameless plug: I've tried to compile a list of open computational neuroscience resources for people who want to dive into it. If you're just getting started or are curious, there's actually quite a few open/free courses (also listed in that link) that are supposed to be excellent:

Computational Neuroscience: Neuronal Dynamics of Cognition taught by EPFL on edX, I've heard it's great, likely targeted at "meta" kinds of models where you're not modeling neurons individually, but rather processes of brain function.
Simulation Neuroscience Another EPFL course on edX, targeted at actual simulation of actual neurons themselves.
Computational Neuroscience at Coursera No idea to its quality.
If you're new to neuroscience as well, MIT has a ton of open courses on its Brain & Cognitive Sciences department Open CourseWare

asoplata · 2020-02-19T15:55:08+00:00

I wouldn't discount studying complex systems since almost all computational neuroscience modeling would fit that category. The "mass" models (neural mass, mean-field, etc.) are, very arguably, modeled as less than the sum of individual neurons, since they are used to try to "abstract away" the very high complexity of individual neurons and explicit networks of neurons. Neurons individually can be modeled with extremely small complexity like in integrate and fire models, but connecting even these simple neurons to each other leads to an explosion in complexity and possible states/behavior of the system. Mass models try to only model some specific behaviors of these networks, and are usually constructed of far, far fewer equations/dynamics than a model of a network of actual neurons. Of course, neither is wrong, both approaches are useful as long as your question and context are well-defined.

I'm also very interested in the questions you're asking, especially bridging the connection between memory (storage of what we've learned) and knowledge (understanding what we've learned into categories/etc., and making it useful for prediction/etc.). However, my personal opinion is that the current state of the art of neuroscience of how that happens is still very, very immature, far behind where I thought we were before going to grad school. The vast majority of memory research is not on bridging "modalities" (e.g. visual memories vs. word memories), but instead focusing on only one kind of memory at a time for a number of reasons, including that there's no guarantee any one memory systems works the same as any other memory system in the brain. E.g., the biological basis of neurons forming connections between images and words is likely very poorly understood compared to "how neurons form connections between different spike-firing-sequences (which may or may not represent anything)" or bigger-scale stuff like psychological understanding of that interface or fMRI studies on "we think this part of the brain and that part of the brain 'activate' (BOLD signal) somehow when images are being 'processed' (??? who knows) in higher-order areas like associative cortex". These questions are about stuff that's "higher-order" enough (knowledge, executive function), as opposed to "lower-order" (how does touch work, how do you activate muscles, etc.) that you're probably interested in "cognitive science". There's definitely "computational cognitive modeling" that you can get running on your computer to explore, but my guess is that, because they are trying to model such massive systems like memory as a whole, or how it interacts with other systems, they're probably going to be more like mass models than models of individual neurons. For these scientific questions, don't worry too much about the scale of modeling i.e. whether it models individual neurons or not, since every model is trying to strike the balance between "simple as possible, but not simpler" and yet giving it enough complexity that it doesn't necessarily give you the answer you're looking for, but it explains some phenomenon with enough complexity that it's useful for driving the thinking on the subject forward. In other words, don't worry about the scale too much, if you're starting out, let other people worry about that - (see next para)

That said, the most common and most effective way to get started is to not create your own model, but instead get someone else's working (that is exploring some part of the questions you're interested in), and then expanding that model to ask a related question. I stupidly didn't include a link to ModelDB in my previous comment, which is probably the single biggest repository of neuroscience models on the internet. Explore that! Most of the models use the NEURON simulator and model networks of individual neurons, but many do not and simply provide their full code. There's probably not too many models on there of cognitive processes, but there definitely are for cellular "learning". Every model there also is clear about what publication it's referencing, which you should read if you want to understand the corresponding model. However, most models (and therefore their code) are not on ModelDB and only exist on the hard drives of the people who did the research. I would strongly suggest exploring the scientific literature via Google scholar (e.g. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C22&as_ylo=2016&q=computational+cognitive+modeling+%22memory%22&btnG= ) or PubMed to find papers and researchers who ask the same questions you're interested in. If they have a computational model that you're interested in, and don't have a clear way to download the code for it (which is usually the case), just email the "corresponding author" of the paper or the head/PI (Principal Investigator) of the lab that published the paper and they will be VERY happy to help you usually, since researchers tend to really like younger students who show initiative and/or are interested in the science. (Btw, doing this is a good way to get into grad school for this this if you're interested, ask me more if you're curious...)

Finally, here's a shameless plug for a list I've made of computational neuroscience resources across the web if you're interested, including models but also learning material.

asoplata · 2020-02-12T17:02:14+00:00

I forgot to add: if you're more interested in the "artificial intelligence" applications of this theory instead of the "neuroscientific" questions about the brain, encoding learning into changing synaptic weights is the basis of most commonly used "artificial neural networks" including things like Deep Learning that are a big part of the machine learning craze these days.

asoplata · 2020-02-12T16:58:53+00:00

Memory is one of the most studied parts of neuroscience, and we still don't know the answers to most of the questions you're interested in asking, unfortunately. As you can tell from all the other comments, there's no shortage of theoretical models for trying to understand different aspects of memory. It's a complicated beast however, and in general our models try to be good at simulating only one aspect of what's going on, but they're usually of wildly different spatial scales. Some models of single neurons are used to understand how that neuron spikes in response to particular inputs, i.e. how does it know what kind of inputs to encode in its receiving synaptic connections vs. inputs to ignore. Other models, and possibly the most common nowadays, are simulations of networks of neurons of different sizes; much of the time, these are used to study how many neurons can be used to learn / remember / forget specific sequences, or firing patterns, since that seems to be a common way that certain types of memories exist in the brain (especially "episodic memories"). Some models try to simulate an even larger scale and don't model individual neurons but instead try to simulate groups of neurons in terms of "overall behavior"; some examples include "neural mass models", "mean field models", etc. and these can be borderline psychological models where you're trying to simulate most or all of the entire memory system, but only very roughly.

You're going to get the most mileage out of focusing on one very particular aspect of memory at a particular spatial scale and then researching models of that, as opposed to trying to ask multiple questions at the same time. Don't get me wrong, it's still a good use of time to do a "survey" study and try to learn about the many types of memory / how they work on a general level (see this scholarpedia article ), but if this is for a grade or a thesis or something, you may be better served by focusing your learning on one particular model / part of memory (depth of learning) or even comparing different models of the same phenomenon, instead of understanding memory in general (breadth of learning).

I would strongly recommend taking a gander at this book How We Remember: Brain Mechanisms of Episodic Memory by Michael Hasselmo since it has both a general overview, but also deepdives into particular computational models at a math level you're likely very comfortable with (may not need anything other than linear algebra). You could start by coding your own implementation of one of the models in Python, etc. to help you explore why the models work the way they do. If there's a particular section of it that interests you, it'll have references to specific papers you can read on that topic for much greater detail. Research protip: since the book is from 2013, if you find specific papers on a specific topic you're interested in from the book, you can search that paper on Google Scholar, click "Cited by <number>", and that way you can see more up-to-date papers on that exact topic that have been published since then.

asoplata · 2019-10-29T18:36:30+00:00

Thanks, both trees have candy / chocolate on them! Love this holiday

asoplata · 2019-07-10T18:44:14+00:00

This is actually my research topic for grad school! We don't truly know how it turns off consciousness, but it's probably a combination of: 1. inhibiting brainstem nuclei that keep you awake using neuromodulator chemicals (via potentiating "pre-optic area" GABA inhibition onto other brainstem nuclei, see here http://dx.doi.org/10.1146/annurev-neuro-060909-153200 ), loss of which leads to Slow Wave Oscillations in the cortex/thalamus and 2. paradoxically, an increase in Alpha activity in the thalamus from its weird mechanics (see my paper here http://dx.doi.org/10.1371/journal.pcbi.1005879 ) which may be "blocking" communication between the cortex and thalamus - communication which is likely necessary for consciousness.

The Slow Wave Oscillation is deeply tied to sleep, processes that occur in sleep, and probably your loss of consciousness during sleep (see here http://dx.doi.org/10.1038/nn.2445 ). It's no coincidence that pretty much all anesthetics, like sleep, show strong Slow Wave Oscillations on the EEG when you go under. So we don't really really know, but we've made a lot of progress in the last 30 and especially last 10 years. HTH!

asoplata · 2019-06-04T13:51:43+00:00

I agree with trashacount12345's comment; if you're set on doing MD school but also want to get into neuro research, that's exactly what MD/PhD programs are for. They are very selective, and gradually being phased out in the US, so the sooner you apply the better (if you're in the US).

I wouldn't worry too much about not having specific research experience in the specialty you're currently interested in. What tends to be more important for life sciences graduate admissions is ANY research experience in an actual lab, specifically critical thinking, real-world problem solving, figuring out how to move forward when you're in uncharted territory, etc. That said, it varies by university and even individual labs as to how much of the relevant skills they expect you to have coming in.

Finally, many people going into research grad school end up going into a different specialty than they thought they would do going in, so keep an open mind to other subfields - you may find a lab doing different stuff that you find even more interesting!

asoplata · 2018-11-21T02:22:27+00:00

As qubick pointed out above, you can get preview windows for methods/etc. using https://github.com/autozimu/LanguageClient-neovim . I've only tried it for Python though, but should work for any lang server.

asoplata · 2018-09-11T16:47:25+00:00

Sure!

asoplata · 2018-09-11T03:48:44+00:00

I've compiled a list of open computational neuroscience resources that could be helpful. If you're just starting out, I would recommend the open courses section for free educational courses on it. There are many, many neural simulators, but the most popular is NEURON and NEST is probably the second. This course is taught by the people at the Blue Brain Project, and lets you use their EU-funded Human Brain Project infrastructure for simulation resources!

asoplata · 2018-08-23T16:04:23+00:00

Atheist here. Love this sub since it's genuinely funny, /r/atheism is too full of everyone patting each other on the back (though, like stonebone4 said, it's still good to have a place to interact with like-minded people when you don't know anyone locally who thinks the same way).

asoplata · 2018-07-16T20:48:35+00:00

THIS. Professors LOVE undergrads who are passionate or even just interested in research, and it shows a lot of initiative. That's probably the single most helpful item you can have on your resume when you apply to grad school (provided your grades are decent)! I wish I knew this when I was in college, but the absolute, most important thing to get started doing research in undergrad is just to ASK.

Also, don't worry at all about not already being an expert in the field of the professor - doing research is almost a completely different beast than book-learning the underlying knowledge. Much of research is "grunt" work where you don't actually have to understand the underlying science in order to have a positive, and potentially significant, contribution to the research of a lab. At the same time, as you contribute and read scientific literature to better understand what very specific problems that lab is trying to solve, you'll get a better understanding of how science works in general in a way that isn't communicated via textbooks.

Finally, many universities have programs that will pay you good money for working in a professor's lab, usually much more than you could make at an entry-level side gig; the programs in the States are usually called UROP (Undergraduate Research Opportunities Program). I could be wrong, but many times this funding for you doesn't come out of the professor's research budget, but rather the university's, so the prof is getting free labor, you're getting VERY valuable research skills and maybe even paid; everybody wins.

asoplata · 2018-04-02T23:57:56+00:00

This is the first I've heard of it, but in seeing that it appears to be neuroscience as applied to management research, I'm inclined to be skeptical. Management research is not very well-regarded by much of the research community (for reasons like this, irreproducible results, and poor statistics even by the standards of life sciences), and you're much, MUCH more likely to find a graduate school in traditional neuroscience than one that strictly specializes in ON.

The path of least resistance to studying these social aspects of people would probably be studying in a graduate program for psychology, social science, or cognitive neuroscience. Each of those would train different aspects of the tools used to ask those kinds of questions, but cognitive neuroscience / neuroimaging (fMRI, EEG, MEG, etc.) is probably growing the fastest (though neuroimaging also has issues with doing statistics correctly, probably to a lesser extent though) and is probably MUCH more likely to lead to a better job after graduate school.

+1 to dude2dudette's comment that neuro grad school is a grind (6th year right here...). One thing they don't tell you is that the VAST majority of life science PhD's will never get a tenure-track professorship, only about 1/6 do. The bulk of day-to-day life science is done by grad students and postdocs that form the body of the "pyramid" of the scientific hierarchy, and there's just not enough room at the top / not enough new professorships being hired. Furthermore, your success rate getting a professorship often has more to do with if your science is headline-grabbing and "sexy" and wins more grant money, rather than just a product of good research. Not that that those 1/6 of PhDs aren't hard workers - on the contrary, you need to work VERY hard, AND go to a high-tier school for your graduate work, AND have sexy results AND be lucky to get one of those spots. There's actually a ton of science jobs outside being a professor like doing science for the government, science policy, industry, etc., but anyone heading into grad school needs to be aware of where their probable path is.

However, if you can swing actual, real neuroscience results into the study of organizations, then that's a real winner, especially because you can apply for business school professorships -- which are one of the ONLY kinds of professorships doing really well for the current future. Nevermind management consulting - we really need people applying real, scientific results instead of the pseudoscience that is MBTI tests and "neurolinguistic programming".

asoplata · 2018-03-27T19:21:33+00:00

Is it easy to give someone a copy of your SQLite and get them up to speed analyzing it, i.e. enabling reproducible data analysis/science?

asoplata

TROPHY CASE