We are Konrad Kording, Megan Peters, Brad Wyble, Dan Goodman, Gunnar Blohm, and Sean Escola, and we're a group of scientists who started a major, international online summer school aiming to democratize science education and make it accessible to ...

How well do the skills you learn in one niche of your research translate to a different one? e.g. Could a student from Professor Escola's lab work in Professor Kording's lab without trouble?

that depends on the research topic of course, but generally yes. Computational skill-sets are quite universal and translate well (Gunnar)

More of a comment than a question . . . but just wanted to pop in and say I VERY appreciate the work you all are doing! I’ve been doing neuroscience for ten years and the default answer for “how do I get started in comp neuro?” has always been “read Dayan and Abbott” even though we all knew it was being rapidly outdated. Thank you for providing the community a better answer with Neuromatch! Also being in a computational neuroscience lab, every year we’ve seen more and more interest in the field and I think this is in large part due to NMA’s success and accessibility. So again, a HUGE thank you to you all!

Thank you so much for the kind feedback! We really appreciate it! <3

(Gunnar)

I have always been interested in computational neuroscience but having to write code in anything other than matlab is so intimidating.. how much programming do I have to know for this and do you train students in it?

Good question! We have precourse refreshers that help you get up to speed in Python, but if you've got a good background in Matlab you'll likely be fine. Both are C-based languages. If you head to https://compneuro.neuromatch.io/tutorials/intro.html and click on the refreshers on the left you can see what kinds of things you'll want to be able to handle to get the most out of the course.
-- Megan

Yes, I totally understand the feeling...

The amount of programming needed to tutorials is quite minimal. You will essentially be asked to complete 1 line of code within pre-written code. (you can check out tutorials here: https://compneuro.neuromatch.io/tutorials/intro.html)

There also precourse refreshers for you. I would recommend you learn the basics of Python prior to the course. But if you have Matlab knowledge, you'll easily learn...

(Gunnar)

I took the deep learning course last year, know Matlab, and felt the exact same fear as you of not knowing Python. Turns out all my pod-mates who knew Python did exactly what I was doing: look up syntax on google 😁 From my personal experience, you’ll be fine! The course is more about learning the concepts I felt, with Python as an open-source tool to understand how to use those concepts.

Stack Overflow is everyone's backup coach, even the experts.

-Brad

100%

-- Megan

Is the course aimed only at university students? I have some data science knowledge and am interested in neuroscience but have no academic training. If I felt comfortable with the pre-course requirements would I still be able to attend?

Students, staff, industry professionals, anybody! You're absolutely welcome to attend if you have the right background!
-- Megan

What would be a good way to learn the necessary Maths to work in a computational neuro setting if you come from a non-Maths background? I’m currently in the middle of a neuroscience PhD but it seems hard to get a grasp of the computational methods without an undergrad phsics/maths degree.

While we have the pre-course refreshers, if you're seeing the material for the first time it will be very difficult for the course to benefit you. If you don't have access to university classes or community college-based courses near you, there are a lot of great resources on Coursera and MIT Open Courseware. You'll want to do Python, plus linear algebra, calculus, and stats. I'll say this though: technically, I never took linear algebra as a formal course in any of my college or graduate training. So it's possible to have a solid understanding of matrix operations, random variables, basic calculus concepts etc and get a lot of benefit from the NMA materials.

Finally, NMA is really a firehose of info so even if you've got a solid foundation, it will probably feel overwhelming. That's totally okay and to be expected. If you do the course, just try to be a sponge as much as possible, and know that the tutorials and materials will always be there for you to revisit later. A crash course for 3 weeks can't replace a 5-year PhD in Computational Neuroscience, but it can give you the "appetizer" version of a lot of the techniques out there so you can come back to it later for a full meal on your own time :)

-- Megan

Is it possible to complete the course while holding a full-time job?

Unfortunately no, the course is a 6 hour a day commitment plus group projects & time for consolidation. The Interactive Track is designed to be "all consuming" for those 3 weeks because it's basically a PhD in a box / firehose of information.

But, if you don't have time or capacity to do the interactive track for 3 weeks straight, you can always do the materials on your own! They're freely available, forever. We are also actively looking for ways to do different types of schedules with TA-led pods, so hopefully this will be an option in the future for e.g. nights/weekends sometimes. We've seen community-led initiatives have success with that already, so we're seeing if there are ways to support those communities too.

-- Megan

I have a neuroscience and psychology background but would love to get into this career path, do you think this could help? I have no programming experience!

I think that taking NMA-CN is the best way for you to get started with comp neuro! This is how many participants change fields / expand their expertise.

(Gunnar)

We try to make the comp neuro course accessible for everyone, so you should consider trying it. We have some preparatory materials to give you the basics of python and you should definitely take a look at those in the weeks prior to the start of the course.

​

When the course is over, you can go back and re do the tutorials to help anchor the concepts. Many people find re-doing these tutorials is helpful.

-Brad Wyble

What math topics do you think are most essential in computational neuroscience?

I think calculus, statistics and probability are probably the most frequently used topics in comp neuro. I'd be curious if any of the other organizers would add something to this list.

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-Brad Wyble

Linear algebra and differential equations (if that's not covered by calculus).

-Dan Goodman

at the risk of exposing myself as someone who likes science more than they are good at it, how hard is it to get into this program?

We aim to accept as many people as we can who have the right background to succeed in the course. Our primary limitations are (a) funding, and (b) # of qualified TAs. If we have enough funding to support enough TAs, then we'll accept literally as many students as possible to fill up those spots! So if you have the right background, please do apply. You'll be asked to self-assess your capacity, and do be honest of course. If you've never seen a for loop and have never thought about a derivative you'll probably have a bad time, but if you have a look at the precourse refreshers (https://compneuro.neuromatch.io/tutorials/intro.html, in the left navigation) and those seem reasonable to you, you might just be fine!

-- Megan

My background is in probabilistic machine learning and I wonder if it is possible for someone like me without any background knowledge of neuroscience to succeed in the computational neuroscience course?

Yes, as long as you're prepared to brush up on neuroscience background prior to the course. We do provide resources for that in our prerequisites materials:

https://github.com/NeuromatchAcademy/precourse

Also, check out the neuro video series: https://compneuro.neuromatch.io/tutorials/W0D0_NeuroVideoSeries/chapter_title.html

(Gunnar)

How is this different from a MOOC?

NMA builds community. We provide TAs, you work with other participants. There is group project work, including mentors and guidance from TAs. It's much more engaging than most MOOCs. Engaging and hands-on = maximal learning and retention. + it's fun! (according to feedback from participants and TAs we received)

(Gunnar)

The students work together in small group "pods" of about 10 students each, led by a TA. The course runs over three weeks during which we have many real time events in addition to the 4 hours of daily pod work: Q&A panels with world experts, professional development sessions, project mentoring, etc.. There's also an incredibly active student Discord. This is much more than a course you do at home on your own schedule. It's about getting deeply immersed in a global community of like-minded scientists for interactive learning and sharing.

(Sean)

Hello! What advice do you have for someone finishing up a bachelor's in Neurobiology but is interested in pursuing an advanced degree in data science with very minimal coding experience? Like what types of positions should one pursue out of undergrad to gain more experience to be a better applicant?

For context, I'm interested in applying machine learning algorithms to neurological data.

It'd be a great start if you could do math / stats / programming through Coursera or similar! Once you've got the basics down, the tutorials at NMA walk you through how to set up lots of "toys" to do the more advanced stuff.

But it's not just about coursework. You'll want to show that you can engage in independent research, too, so see if you can do a paid research assistantship somewhere. For example, in the years between undergrad and grad school I managed a lab for 2 years to get more experience and it was truly invaluable -- both because it gave me research experience, and because it helped me narrow down exactly what I wanted to do for my PhD and career.

-- Megan

I suspect it would be hard to find a job that just pays you to do learn coding, so if formal education isn't a possibility, I think the first thing you need to do is learn more about coding, either through the pre-course prep materials that we provide, or free tutorials online, or as MOOCs (UDEMY has fairly cheap courses if you catch one of their 90% off sale days, which are frequent).

You can start building a portfolio of side projects on github to establish your expertise as you grow your skills. Also, get on LinkedIn and look for positions that you'd like and see what they are asking for in terms of skills.

​

-Brad

How much should I expect to pay for a course?

That depends on several factors. We have a base rate for funded participants (i.e. you're part of an institution that will cover costs) and a separate rate for self-funded participants. Those rates are adjusted to your local cost of living. But if cost is an issue you can also "pay what you can". We might ask you to explain so that we can better understand our applicants' circumstances and help future participants (i.e. we always want to learn and improve).

Our main goal is to make NMA accessible for everyone!

(Gunnar)

Read more on our FAQ as well: https://academy.neuromatch.io/faq

Have you found at all that being fully online makes the lessons stick less?

No, students work in small groups (pods) with TA support and we leverage peer programming and the Socratic method to together with group discussions etc to ensure participants enjoy the course.

Also, what really allows you to learn (and lessons to stick) is your hands-on engagement with the materials through the coding tutorials.

(Gunnar)

This is Gunnar: let's get the party started!

Hello! Sean Escola here.

Apart from just the advantages of learning about Neurotech, what specific career/academic opportunities do you think might open up after participating in the course? Asking specifically if someone is working full-time at a FAANG company, if they should take a break and pursue this seriously.

That's a question that is hard to answer. The tools we teach will allow you to dig deeper in research but also give you new means to address questions in industry. E.g. we have had many participants from industry.

During NMA, we typically have a job fair, we run a job board and we have potential employers specifically looking for people with the skills that we teach. So in that sense we provide direct opportunities. But it also shows that employers value the skills we teach.

Wrt academia, many labs now view it as a big + when applicants have participated in (or have TAed for) NMA. And NMA alumni tend to do well (as far as we can tell after 2 years...).

(Gunnar)

1. If you had to pick just one paper/study/article to sell someone on your field, which would you pick?
2. What exactly is a "neural network simulator"? Are we talking about the same neural network as in machine learning or is this a literal network of literal neurons?
3. I can kind of imagine it for movement, but I'm super curious how you study something like causality, uncertainty, or consciousness with machine learning.

Re 2, I guess I (Dan) should answer that one since I wrote one. "Brian" simulates what networks of biological neurons would do. Artificial neural networks don't need simulating because they just are what they are. But spiking neural networks are (a) models of a real thing, (b) need simulating because the models are typically mathematical equations that can't be solved exactly so you need to do some approximations.

Re: 1. A good example is the Reynolds & Heeger model of normalization. https://www.salk.edu/pdf/faculty/Reynolds_Normalization_Model_of_Attention.pdf

2: It could mean a lot of different things. Dan's Brian simulation is a fairly realistic model of spiking dynamics, and even more complex ones can be made with the package Neuron. But there are also much much simpler models that count as NN's. For example, deep learning and perceptrons have no temporal dynamics at all.. each neuron is literally just a single element in a vector that accumulates values. These are perhaps the simplest possible version of a neuron.

1. These are emerging areas. I can speak to one of our models, in which we use an autoencoder to simulate visual imagery https://arxiv.org/abs/2112.06832. It is hard to verify these models, but they provide a way of thinking about these very thorny questions.
   

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-Brad Wyble

I'll answer (3) because it's my area!

I study uncertainty, metacognition, & consciousness in my lab using psychophysics, brain imaging, machine learning, and computational models. We present people noisy information in various ways many times, ask them to identify that information, and then measure the consistency of their responses and how that consistency relates to their sense of uncertainty or confidence. We also do this in an MRI machine or with EEG or fNIRS and see what patterns of brain activity are predictive of choices and judgments of uncertainty, as well as the uncertainty in the information itself.

If you like, you can check out some of the media posted on my lab page to see some recorded talks about specific projects, here: https://faculty.sites.uci.edu/cnclab/

-- Megan

Neuroscience admissions and #GRExit: with recent reports of MIT reinstating SAT requirements for undergrad admissions, what are your thoughts on neuro/biomed programs discontinuing the use of GRE in admissions?
While GRE is an indicator of "wealth" more than anything else, aren't other opportunities (research assistantships, etc.) similarly inequitable, especially for international students?

Yes I think that other indicators (letters, etc) are also hugely skewed by inequity. In my personal opinion, GREs should be an option so that students can put this forward if it reflects well for them. We do this with everything else when we build our CV, by providing a list of things that we have done or earned. GREs should be no different.

-Brad

Personally, I would agree with your statement, but that's just as an aside.

At NMA we thrive to be maximally inclusive. You can participate unless we think your background is not appropriate and thus you would not benefit from the NMA experience.

(Gunnar)

What are some top things that you look for when hiring PhD students in such a multidisciplinary field? Asking for a friend 👉👈

Ha ha, everyone has different criteria... Personally I do of course look at your skill set, but maybe more importantly I want to assess your potential, your motivation, your enthusiasm. I have found that enthusiasm is the best predictor for success. You can learn what you don't know yet when you're motivated. A PhD "student" is indeed a learner and I very much encourage maximizing learning opportunities.

Also re skills, many times you'd be working in a multi-disciplinary team where everyone contributes something different. I like learning from my students, so I hire from all kids of backgrounds...

(Gunnar)

I first heard about Neuromatch Academy through a neuroscience podcast, and I'm definitely planning to apply for one of the courses this summer (although I haven't decided which yet!).

I have two questions for the panel: 1) Are there common critiques you get from reviewers regarding the computational modeling of more abstract concepts, such as action, perception, agency, learning, etc.? My background is in motor control, but I'm deeply interested in how movement is modulated by some of these "fuzzy" variables - like sense of agency, for example. I'm just hesitant that my approach to these topics might get too speculative, so I'd be interested to hear your advice on modeling these "difficult-to-quantify" topics while maintaining a strong mechanistic foundation.

2) Do you have any advice to share for those of us who are junior faculty - specifically, is there anything you wish you could tell your pre-tenure self? I am completing my PhD this August, and I'll be starting a tenure-track faculty position immediately after. This is exciting, but also tremendously terrifying. Any advice is much appreciated!

Thank you to all who are part of this panel, and thank you for the work you've done with increasing global accessibility to computational neuroscience!

re 1: part of NMA-CN teaches "how-to-model". You can totally make progress with abstract concepts. In fact, many of us do. The question is always how to you "measure" those concepts? Even in a model, you need to somehow define them, and ideally link them to something measurable (now or in the future).

re 2: congratulations! Many people probably have different pieces of advice here... to me, the most important part of academia at all stages is learning new things. Don't forget to learn! For many a postdoc is a key time of learning something new / complementary. Since you'll be skipping that, try find time now or as a faculty to expand your expertise, maybe through a fun collaboration?

Thank you for your kind words!

(Gunnar)

As pre-tenure faculty myself putting in my file in the fall, I (Megan) hear you about (2)! Congratulations on your position! Some thoughts:

1. Your email box will be worse than you can possibly imagine. Filters are your friends.
2. Learn to say "no" to unpaid/unofficial labor when it doesn't make sense to say yes. If it's an official committee, great, but this time is all about you and your research, so prioritize getting that off the ground first!
3. Ordering equipment and doing lab renovations during covid is a pain in the butt. Order stuff early. It WILL be delayed. Chase it up.
4. Participate in the graduate recruitment process as much as you can, not just for recruiting to your own lab but also to help build the cohort of incoming graduate students. These will be your colleagues for your entire pre-tenure period, so it's good to get students into your department who are engaged, motivated, and capable.
5. Make sure you have a faculty mentor (or mentors) both within and outside your department, so you can ask questions and get advice on the tenure process from multiple perspectives.
6. Keep your previous collaborations alive, but try also to branch out on your own and establish your independence. Tenure committees look for evidence that you aren't just riding your previous supervisors' coattails still.
7. Set boundaries for responding to student emails as you teach. It's okay if you don't respond at 10pm on a Tuesday -- it can wait.
8. Protect writing/research development time with your life. Stack your meetings so you can have a day of "get all the meetings done" and then a whole morning/afternoon of grantwriting, manuscript writing etc. The meetings can get out of control quickly, so make sure you don't end up with 30 minutes here and there to try to get your own stuff done because it won't be enough.
9. Lean on your friends and colleagues who have similar experiences. You don't have to do it all on your own -- ask for help!
10. Breathe, you got this :)

-- Megan

Why aren’t you more transparent about your pricing?

We have been continuing to develop our pricing structure for this year, but we are keeping the same basic policy as last year, which our website describes: we have a full price that is weighted by your local cost of living according to country of residence. Moreover, we have a pay-what-you-can structure, allowing you to pay less than the full price, even all the way down to zero if you are unable to pay anything.

The FAQ provides more details:

https://academy.neuromatch.io/faq

-Brad

What do you think of the state of education in the US right now?

If you could change it overnight in some way, what would that be?

Pay teachers more. A lot more. We are strangling our education system in the US and it has massive bad effects.

-Brad Wyble

given whats going on do you accept russian students? im not russian fyi

There are currently no obstacles to students in Russia enrolling in the course and we don't anticipate any on the horizon either. It is our goal to make NMA accessible to everyone regardless of circumstance.

-Brad

Why is this better than the already free neuroscience courses and programs available online?

That's a really great question.

NMA provides personalized small group instructions, with a group of 10-12 peers, and an exclusive paid TA for each group who will help to guide the learning experience. The groups are matched for language, topic interest and skill level. We also provide a live professional development workshops with leaders in the data science and comp neuro fields, as well as opportunities to network with companies.

Furthermore, our materials are presented in a live-notebook format that includes embedded nano lectures along with exercises. Previous students of NMA have reported extremely positive experiences, and our completion rates are much higher (~85%) than standard free courses due to the personalized experience.

You can read more here

https://www.sciencedirect.com/science/article/abs/pii/S1364661321000954

Please feel free to send me an email at [[email protected]](mailto:[email protected]) if you would me to email you a copy of this article.

-Brad

Have any of you gotten you finger stuck in a beaker?

Who hasn't!?

-Brad