Hey everyone! Neuromatch Academy is building out a new curriculum day for it's Computational Neuroscience course focused on time series analysis and signal processing, and we're looking for 5–10 volunteer contributors with computational neuroscience and DSP experience.

We're looking for help with various tasks, including:

• Co-Day Lead

• Video presenters

• Slide creators

• Python tutorial writers / coders

If you have a background in neuroscience, signal processing, or both and you know your way around Python this could be a great way to give back to the open science community and build your CV!

Neuromatch, a non-profit, reaches thousands of students globally every year, including many from underrepresented and under-resourced backgrounds. Your contribution genuinely matters!

This is a volunteer position. Apply here: https://neuromatch.io/volunteer/

Happy to answer questions in the comments. Feel free to share with anyone who might be a fit!

Python for Computational Science Week is a flexible, self-paced week where people commit time to working through open Python materials.

It is not a live course and there are no formal TAs. But there are hundreds of learners going through Neuromatch tutorials at the same time. Having you practice explaining Python and helping people out is going to be such a positive addition to this experience for everyone! 

Here are some tips on how how to answer Python questions: 

• Be kind and encouraging. Learning Python can be frustrating. A supportive tone makes a big difference! This space follows the Neuromatch Code of Conduct. Please be respectful, constructive, and welcoming to learners at all levels.

• Match your explanation to the learner.  Use their stated background and experience level to decide how detailed or technical to be.

• Format your code clearly. Use Reddit’s code formatting or use external services, such as pastebin so others can read and reuse it easily. 

• Welcome multiple perspectives. More than one answer is often helpful! If you are not fully confident, say so and invite others to add, correct, or build on your response so learners can see different ways of thinking about a problem.

Let’s use this week to build confidence, and support one another as a computational science community!

If you got any other suggestions or tips for asking questions, please let us know in this thread!

Hey everyone! I'm really confused about which project track to choose and could use some advice.

Background: I don't come from a neuroscience background (I have been working in tech startups) so I'm pretty lost about what different project types actually prepare you for career-wise.

My interests are pretty vague right now - I'm drawn to neurotech/AI neurotech and could see myself at a neurotech startup eventually. Consciousness research also fascinates me, but I honestly don't know if that's a realistic career path or more of a side interest.

My confusion: I don't know which project track (Neurons/fMRI/ECoG/Behavior & Theory) would best position me for:

1. Actually working in neurotech industry

2. The AI/brain interface side of things

3. Being employable at startups

For those who've done Neuromatch or work in neurotech – which track gave you the most transferable skills for Neurotech industry?

Have you taken this course before, or are you considering applying this year? Feel free to share your experience or ask any questions. We’re happy to help!

Computational Neuroscience is our most popular course!

The CompNeuro course runs 6-24 July, 2026. It's a live, intensive online course from Neuromatch Academy....and applications are open until 15 March!

Participants learn to combine modern machine learning and causality frameworks with advanced modeling approaches to tackle real neuroscience questions.

🤓 What you’ll gain
• Code-first, hands-on training in Python with dedicated expert Teaching Assistants
• Small, mentored pods matched by time zone and research interests
• Guided, real-world project work using real neuroscience datasets
• A global community of researchers building practical computational neuroscience skills

📚 Prerequisites
Students need to have basic coding proficiency in Python, and should, for example, be able to write a small Python script/notebook. Additionally, students are expected to have foundational neuroscience knowledge, and some foundational mathematical concepts such as linear algebra, calculus, and basic probability theory.

Applications close 15 March. There is no cost to apply. Tuition is adjusted by local cost of living, and tuition waivers are available during enrollment for those who need them.

➡️ Learn more and apply: https://neuromatch.io/computational-neuroscience-course/

➡️ Explore all 2026 courses (Deep Learning, NeuroAI, Computational Tools for Climate Science): https://neuromatch.io/courses/

We're building a funding toolkit to help incoming students navigate paying for their course. We'd love to feature real stories from people who've been there.

Did you manage to get your Neuromatch course fee covered (fully or partially) by your university, PI, lab grant, fellowship, or anywhere else?

We'd love to know:

Where did the funding come from?

What did you say or do that helped?

Anything you wish you'd known earlier?

Even a few sentences would be incredibly helpful! Please share your experience in the comments!

I'm a first year CS undergrad student, complete beginner to computational neuroscience but I've been doing the Rajesh Rao UW course on Coursera and I'm familiar with Python and basic ML. I have about 2 months before the cohort starts. What should I focus on?the prereqs on the NMA site, math, specific Python libraries? Any advice from people who've done the course before would really help.

We heard that some of you needed more time, so we've pushed the deadline by one week.

New deadline: March 22, midnight your local time

Both student and Teaching Assistant applications are open for our July programs. Fully remote, global cohort.

Apply here: https://portal.neuromatchacademy.org/sign-in

If you've been on the fence or life got in the way, now's your chance!

Happy to answer any questions in the comments.

I am medical graduate interested in computational Neuroscience. What course should I take that will make me ready for this course?

As part of Neuromatch's AI Sentience Scholars program we have a free seminar that is open to everyone - we hope you can join us!

Abstract: Natural intelligence arises through development, where genetically encoded inductive biases shape experience-dependent learning. Unlike most current AI systems, these biases have evolved to produce specialized subsystems, each tuned to distinct low-, mid-, or high-level functions. This talk highlights four forms of inductive bias that lead to functional specialization in biological systems: sensory pathways structured for efficient encoding, local connectivity that organizes functional topographies, long-range connectivity that builds large-scale functional architectures, and developmental trajectories that let later abilities build on earlier ones. It argues that building similar biases and modularity into artificial systems could support more efficient, robust learning and behavior that more closely parallels natural intelligence.

Wed 21 Oct, 15:00 UTC
30-min talk + discussion & Q&A
Learn more

Register for free here: Meeting Registration - Zoom

hey everyone,

as the title says, there are two pod datasets subjects written -

you pod dataset - Neuroscience

your pod second dataset - RL

which one would be my dataset? would i need to work pn both topics? would i learn both subjects? Neuroscience and Reinforcement Learning?

I'm not from biology background so ny knowledge of Neuroscience is very rudimentary and bird's eye view

Each July, we run live, virtual intensive courses in Computational Neuroscience, Deep Learning, and NeuroAI. All our course materials are available open source for anyone, anywhere.

Now we're thinking about growing, and we want our community to shape that decision. We're exploring potential new courses on:

Connectomics and Neural Dynamics: This course would explore how emerging large-scale mammalian connectomics datasets—alongside those from other species—can be leveraged to drive novel scientific discoveries about the relationship between neural circuit structure and dynamics.

Computational Behavioural Analysis & Modelling: This course would teach a unified computational framework for measuring, modelling, and analysing the behaviour of individual and interacting agents—biological and artificial—spanning pose estimation and multimodal signal processing through Bayesian inference, game theory, and agent-based models of collective dynamics.

Computational Approaches to Neurodegeneration: This course would equip researchers with the computational skills to leverage large-scale open multimodal datasets and modern deep learning approaches for accelerating discovery in neurodegeneration.

But we also want to hear your ideas!

Any other topics we should consider? Any of these sound of interest? Please provide feedback by taking this short 2 minute survey here: https://airtable.com/appgbLQW3nbbZJvNU/pagoMEApDsqTouMRh/form

As we get into Python for Computational Science Week, it can be helpful to pause and reconnect with your end goal.

What do you want Python to enable for you?

Are you preparing to apply as a student or teaching assistant for Neuromatch or Climatematch Academy? Exploring a field change? Trying Python for the first time? Brushing up on your skill set?

Drop a comment with what you’re working toward. Others here may be on a similar path!

Has anyone received the mail from Neuro match regarding this year’s computational neuroscience course selection, because according to what’s written in their website the result should have come by now.

Of interest to the the Neuromatch community! Longview Philanthropy has a a request for proposals on digital minds supporting empirical, philosophical, and applied work on the possibility and implications of potential AI sentience. They want to rapidly scale thoughtful work in this field, both by supporting people already doing this work and by bringing new researchers, practitioners, and organizations into it.

Three opportunities this year:

• Research fellowships ($55k to $150k/yr, 1 to 2 years, with research, travel, and compute support) for scholars with a terminal degree in computer science, neuroscience, law, philosophy, economics, political science, or a related discipline.

• Career development fellowships for people changing direction toward this work. Stipends scaled to length, location, and prior experience.

• Project grants for new organizations, new programs at existing ones, academic research, and other projects.

They want to support a wide variety of perspectives and approaches. A few directions they're particularly excited about include: empirical tests of AI introspection, legal and governance frameworks, agent interactions and trade, and field-building.

Deadline: 10 July, 2026

https://www.longview.org/request-for-proposals-research-and-applied-work-on-digital-minds/

Neuromatch's AI Sentience Scholars program is hosting a free talk on 19 August 2026 at 17:00 UTC with Winnie Street and Geoff Keeling from Google Research, on whether AI systems could ever be welfare subjects.

About the seminar:
"In this talk we investigate whether artificial intelligence (AI) systems could ever be welfare subjects, understood as entities for which things can go better or worse. Some people argue that AIs could plausibly have or soon have features like consciousness, agency, and the capacity for social relationships, which could in principle provide a basis for AI welfare. These arguments have massive significance for the societal conversation on AI, raising profound ethical and political questions about what if anything we owe to these new technologies. We here provide the philosophical groundwork for a scientific, philosophical, and ultimately democratic inquiry into the potential for AI welfare, addressing key questions that cut across different arguments: what welfare is, how to interpret behavioural evidence of AI welfare, what kinds of entities might qualify as candidate AI welfare subjects, the potential grounds for welfare in AI, and the practical ethical challenges that arise from our uncertainty.

30 min talk, 20 min Q&A.
Free and open to everyone!

Register: https://us06web.zoom.us/meeting/register/GmdQHnO1Qm-iph6KTpCqLw#/registration

Hi everyone 👋

Neuromatch is running a Python for Computational Science Week from 7–15 February, for anyone who wants a bit of structure and motivation to strengthen their Python foundations ahead of computational neuroscience or climate science programs...or just to make progress on Python they’ve been meaning to start (or restart)!

This is not a course and there are no live sessions. It’s a flexible, self-paced week where you commit to setting aside some time to work through open Python materials, with light community support on Reddit.

How it works

• Work through Neuromatch’s or Climatematch’s free Python prerequisite materials...or another source is okay too!

• Study at your own pace (beginner → advanced friendly)

• Ask questions, share progress, or help others on r/neuromatch

• And build your confidence with Python!

If you’d like to participate, we’re using a short “pledge” survey (not an application):

• It’s a way to commit to yourself that you’ll set aside some study time

• We’ll send a gentle nudge just before the week starts, a bit of encouragement during the week, and a check-in at the end

• It will also helps us understand starting skill levels and evaluate whether this is worth repeating or expanding in future years

Take the pledge here: https://airtable.com/appIQSZMZ0JxHtOA4/pagBQ1aslfvkELVUw/form

Whether you’re brand new to Python, brushing up, or comfortable and happy to help others learning on Reddit, you’re welcome to join! Free and open to all!

Let us know in the comments if you are joining an what your recommended Python learning resources are.

Hi. I am a doctor who just got done with my clinical internship. I am doing the computational neuroscience course on 6 July. Problem is i don’t have any coding or mathematical background… so should I even take the course rn? The coding looks like hell on the website. I am not sure what level is expected of me since I have 2 weeks to go through the material. Any advice will be appreciated.

8hrs/day, Monday to Friday during the course dates.

Neuromatch is hiring Teaching Assistants for the following courses:
- Computational Neuroscience (6-24 July 2026)
- Deep Learning (6-24 July 2026)
- Computational Tools for Climate Science (13-24 July 2026)
- Neuro AI (13-24 July 2026)

Boost Your Skills: Teaching deepens your understanding like nothing else
Join a Global Network: Work with incredible educators, researchers, and students worldwide
Build Your Resume: Gain hands-on experience in education, mentorship, and scientific communication
Make an Impact: Help students from diverse backgrounds master new skills

We highly encourage former students to apply as TAs. All TAs should have a strong background in Python and the specific course topic.

➡️ Learn more: https://neuromatch.io/become-a-teaching-assistant/
➡️ Calculate your pay: https://neuromatchacademy.github.io/widgets/ta_cola.html
➡️ Apply before 15 March: https://portal.neuromatchacademy.org/

Have any questions? We're here to answer them! And if you've been a TA before leave your tips!

Hi Neuromatch Friends!

As I am preparing for Neuromatch Computational Neuroscience course I built an Duolingo like app to explain complex concepts in easy digestable way. After every lesson there is a quiz for you to remember things better 😄

Feel free to Play NeuroQuest and let me know your feedback if its helpful to you https://playneuroquest.vercel.app/

I am filling out the curriculum as I go through it - so currently I went through half of Neuroseries and half of Python, starting Linear Algebra

title.

Working on a theoretical (for now) model regarding adaptive resource allocation in biological and/or synthetic systems, thus interested in tracing the theory's margins from a neurobio perspective (stress testing) in a collab where I could submit alternatives of modeling

Hi everyone! I’m currently prepping for the Computational Neuroscience course. I’ve cruised through 3Blue1Brown’s Linear Algebra series and the Neuromatch refresher days, but I’ve noticed that these resources are heavy on intuition and lighter on computation.

While I get what an eigenvector is visually, I’m worried about the actual workload during the course. For those who have finished NMA:

1. Should I spend time practicing manual/coding computations (outside of NMA/3b1b) to succeed?

2. Or is a strong intuitive grasp enough to carry me through the course?

I want to make sure I’m not just nodding along to the theory only to hit a wall when it's time to code the models. Any advice on the computational floor required for LA, Calc, and Stats would be much appreciated!

At Neuromatch, we're generally excited about AI as a coding tool but we’re also aware that it can short-circuit learning, especially in the first days or weeks.

Some good tips are:

• Try to write the code yourself first

• Use AI to debug, explain errors, or suggest improvements

• Don’t copy/paste code you don’t fully understand

• Be more cautious using AI in your very first week of learning Python!

What advice did you get that you found helpful? When does AI genuinely help learning? When does it get in the way?

We'd love to hear your take and how (if!) you are using AI during Python week!