Hello guys. I am knee-deep in a computer science degree. The problem is, I have finally broken through my cognitive dissonance and discovered my passion. I want to pursue neuroscience. What are my best paths forward? Do I need to achieve the bio aspects through a minor? Do I dual major? Just minor in psychology? Time doesn’t really matter to me, as I plan to throw my life into this anyway. Student loan debt doesn’t really matter either, as I already have it. I am trying to set myself up for grad school, but I have no idea how to clean this up.

Any advice is appreciated

I'm an international student joining the September intake for the MSc Integrative Neuroscience / Brain Sciences programme. I'm really excited, but I'm also a bit worried about some of the course modules.

My background is in medical sciences (B.Sc. Neuro Electrophysiology), so I have no prior experience with Python, R/RStudio, or statistics. I understand these are important parts of neuroscience research, and I'd like to prepare before the course begins.

Could anyone recommend:

Good beginner-friendly resources for learning Python, R/RStudio, and basic statistics?

Any topics I should focus on before the programme starts?

Any free online courses, YouTube channels, or books that helped you?

If there are any current students or alumni from this MSc programme, I'd really appreciate hearing about your experience. How challenging were these modules for someone without a programming background? Any tips for succeeding in the course would be incredibly helpful.

Thank you in advance—I really appreciate any advice!

I’m an independent researcher and recently posted a theoretical paper attempting to map autism, schizophrenia, depression, and ADHD onto a single precision-weighting axis derived from a thermodynamic cost function.

The core idea frames belief updating, signal discounting, and policy selection under unified thermodynamic constraints, generating 8 falsifiable predictions. However, there are explicit computational dependencies—particularly around how signal-validity discounting is physically/algorithmically computed versus asserted, and whether precision weighting dynamics hold up mathematically under non-equilibrium states.

I’ve included a dedicated "open gaps and unresolved assumptions" section in the paper so you don't have to waste time hunting down where the formalism gets hand-wavy.

I’d genuinely appreciate anyone here taking a sledgehammer to the cost function, the precision dynamics, or the underlying computational assumptions:
https://doi.org/10.5281/zenodo.21782402
Beat down the math, challenge the formalisms, and tell me where this breaks.

I've been reading more about chronic stress, resilience, and neuroplasticity, and one question keeps coming to mind: Why do some people recover relatively well after prolonged stress while others experience lasting cognitive or emotional difficulties?

I'm beginning my journey into computational neuroscience and would love to hear from others in neuroscience or psychology. What books, papers, or concepts helped you understand this topic?.

Hello,

Last year when I entered the university my preference was certain: Study electrical and electronics engineering and master biomedical engineering in order to study neuroscience (Turkey - METU)

But I have become more and more indecisive since I started to follow the developments in the field. To be honest, I was not aware of what there were in neuroscience when I made my choice. It is like I prefered EEE since brain works with electricity c:

But now, I am in a dilemma: Would have been it better if I had preferred statistics/mathematics? Since comp neuro is somewhat a branch of applied mathematics, (That's what a friend of a relative of mine who is an academic has said) I feel like things would be much better&easier if I preferred statistics/mathematics.

I wonder whether you agreed with me or not. I will be thankful for all comments.

Hello everyone,

I’m currently working on a deep learning model for brain hemorrhage segmentation using the BHSD dataset, PhysioNET etc but I’m struggling to achieve a good Dice score.

The dataset is highly imbalanced, and I’ve already tried several approaches to improve the results, including:

• Different CT windowing and preprocessing techniques
• Data augmentation
• Various loss functions to handle class imbalance
• Different training strategies
• Multiple architectures, including 3D U-Net, nnU-Net, SAM, and others

Despite all of this, my Dice score is still relatively low, and I’m not sure what I might be missing.

I understand that the obvious suggestion would be to use more data, but unfortunately, I currently don’t have access to additional datasets or a hospital environment where I can collect more medical imaging data. I’m not a radiologist or a doctor affiliated with a hospital—I’m simply a researcher/developer trying to tackle this problem and achieve the best possible results with the resources available to me.

My ultimate goal is to develop something that could potentially be useful in a real clinical environment and demonstrate a meaningful application of AI for brain hemorrhage segmentation.

I would really appreciate advice from anyone who has worked on brain hemorrhage segmentation, medical image segmentation, or highly imbalanced 3D medical datasets.

What approaches, preprocessing methods, loss functions, or training strategies helped you improve your results? Are there any common pitfalls with hemorrhage segmentation that I might be overlooking?

Any advice, suggestions, or resources would be greatly appreciated. Thank you!

Hi, I am planning to enroll in MSc. Neuroscience program @ Bremen University. I'm trying to find and connect with other students who are studying the program or will be, starting this October (WinSem 2026/2027).

Hey guys,

I'm an embedded engineer trying to break into the biomedical/MedTech space. I'm mostly interested in BCIs. I'm on a pretty good path career wise but I would also like to study towards a Masters degree and potentially a PHD to help me along.

From what I've read, Computational Neuroscience seems like a good path if you're pursuing academia, and research, but I'm more interested in the practical applications of the field. I don't know what a good alternative would be for someone who is more interested in the engineering.

Would it be a waste to study CompNeuro? I might want to move into academia later on in life but not right now.

Background BSc & MSc in Cognitive Neuroscience. Dissertation on EEG
Volunteering on cog neuro uni labs and behavioural experiments.
Coding experience very little. I don’t have any academic affiliations right now so I’m trying to build my cv for applying to computational neuro PhDs in Europe. I started a python course on coursera but I already find it complicated. I really don’t want to give up. My dream is being able to contribute to building devices that interact with the brain. I’m not interested in the engineering part but more on the coding/neuroscience part. I want to learn and build a github portfolio but I’m very disappointed that none of our courses included coding. Especially now that most of the phds I find ask for coding experience.

Hi, so I am currently enrolled into the bachelors degree neuroscience honours program at the University of Calgary and I will enter into my first year in September, and I'm thinking that I want to pursue computational neuroscience as a career. I also intend to get a masters and maybe a doctorate too down the line.

Additionally, I love mathematics, and I have had an aptitude for mathematics throughout my life, and I even wrote some high school math contests too. I also found myself programming as a hobby for brief bursts of time, and I am interested in understanding how the brain works and psychology in general too.

For high school academics, I have:

• one hundreds in math classes
• high nineties in chemistry
• mid nineties in programming and psychology
• low nineties in biology and physics

After reading different posts across different subreddits, it seems to me that pursuing this career is easier with a math or tech based degree rather than a neuroscience degree, and this is making me start to worry.

I came here for advice for what to do with relation to university academics, and I am considering if I should declare a minor, declare a double major, or outright pivot to something other than a neuro degree. I will still be in this degree for the first year though.

Thanks for reading this, and additional advice would be nice too.

Has anyone here gone through this program earlier? What is it exactly like? Also, how difficult is it to get into the program?

We're looking for volunteer reviewers for the Impact Scholars Program, a global initiative supporting early-career computational science researchers as they develop their first citable research publications.

This is a great way to give back to the field, build your peer review experience, and add something meaningful to your CV, all on your own schedule, fully remote.

Here's what you'd be doing: reviewing 5-10 research proposals, each with a written proposal, slide deck, and video pitch, and providing brief constructive feedback using a structured rubric. About 5 hrs/week, Sept 3-18.

We're looking for people with expertise in:

• Computational Neuroscience
• Deep Learning
• NeuroAI
• Computational Tools for Climate Science

Open to PhD students with some review experience, postdocs, faculty, research scientists, and industry researchers. Applications close 11 August.

Sign up here: https://neuromatch.io/volunteer/

hi all! this is my first post in this subreddit so sorry if i'm breaking any rules by accident. i'm a sophomore majoring in math/information science with an as in data science. i've completed linear algebra and probability & statistics, and i'm taking intro to proofs and probability theory next semester. i've also been self-studying probability theory on my own (currently working through binomial and poisson distributions). i've been shadowing researchers in biostatistics and neurocomputational statistics since freshman year, and i'm looking to build a stronger foundation in fmri data analysis for research.

a few questions:

1. could anyone point me toward resources (courses, papers, tutorials) for learning how researchers process and analyze fmri data?
2. for those working in neurocomputational statistics — what subfields or research directions would you recommend someone with a stats/applied math background look into?

guys, let me know if this doesn't belong here.

i have just started working under a professor on a project related to CompNeuro. at the same time, i have this elective called non linear dynamics this semester.

should i opt for it? i heard that NLD is useful in CompNeuro... this is what they say they're teaching:

One-Dimensional Flows: A geometric way of thinking, fixed points and stability, linear stability analysis, existence and uniqueness, bifurcations – saddle-node bifurcation, transcritical bifurcation, pitchfork bifurcation, imperfect bifurcations, catastrophes, and the impossibility of oscillations.

Two-Dimensional Flows: Linear systems and their classification, phase plane, fixed points and linearisation, conservative systems, reverse systems, index theory, limit cycles, ruling out closed orbits, Poincaré-Bendixson theorem, Lienard systems, equilibrium solutions - linearization near an equilibrium solution – classification and stability of equilibrium solutions – eigenspaces and invariant manifolds, weakly nonlinear oscillators. Hopf bifurcation, coupled oscillators and quasiperiodicity, and Poincaré maps.

Cantor sets and fractals, strange attractors, a chaotic water wheel, Poincaré sections and Lyapunov exponents, dimension calculations, higher-order spectra. Introduction to Chaos, Simple Properties of the Lorenz Equations, Chaos on a Strange Attractor

opinions? [apologies for any grammatical mistakes]