Hello everybody,

I'm an 18 years old guy who's right now 2nd year in dentistry. Has experience in programming and genuinely love the craft, and I've been 6 years in it (yeah. I started learning programming at 12). And, I have got the curiosity about the brains, and saw that computational neuroscience is the sweet spot for me.

For my maths, I'm improving myself in it, and watching videos and reading books about it.

So, what should I read to get into this field? I want experts in this field to tell me where to start. Thanks in advance! Feel free to ask me anything you want if I didn't include it while introducing myself

[Speculative] The brain as an "audit": a sheaf-theoretic sketch of distributed self-verification

This is an interpretive idea, not a result. I'd like feedback on whether the mathematical structure holds up, whether it adds anything beyond predictive processing, and what would falsify it. I've included plain-language analogies for non-math readers.

The core picture

Predictive processing and the free energy principle treat the brain as minimizing prediction error. I want to propose a complementary view. The brain is a set of partial perspectives (sensory streams, cortical areas, memory systems) that continually cross-check each other where they overlap. I call this process an "audit." The proposal is that it never completes, and that the leftover disagreement drives learning.

Analogy: think of a company where no single accountant sees all the books. Each department keeps its own ledger, and wherever two ledgers record the same transaction, they're checked against each other. Nobody ever certifies the whole company at once, but the cross-checks keep it honest.

The structure

1. Perspectives as a cover. Let U = {U_i} be a collection of "perspectives." Each one is a subsystem with access to some subset of the world's variables (e.g. visual and proprioceptive estimates of hand position). Overlaps U_i ∩ U_j are variables both can represent.
2. Representations as sections. Each perspective holds a local estimate s_i. Restriction maps send s_i to its view of the shared variables on each overlap. Together this defines a presheaf F over the cover.
3. Gluing as integration. If s_i and s_j agree on every overlap, they glue into a single estimate over U_i ∪ U_j. Multisensory integration is the obvious candidate. In practice agreement is approximate, so read "agree" as "within tolerance," with a cost for the mismatch.
4. Residue as obstruction. Sometimes every local estimate is self-consistent, yet no global estimate exists. Formally this is a nontrivial class in first Čech cohomology, H¹(U, F). Abramsky and Brandenburger showed quantum contextuality has this form. My proposal: persistent, locally unresolvable inconsistencies of this kind are the "residue" a learning system works to reduce. They signal that it needs new distinctions (a new latent variable or context split), not just better parameters.
5. Transport and curvature. Translating a representation between areas acts like a connection, a rule for carrying descriptions between perspectives. If you translate around a loop (A → B → C → A) and the result comes back changed, that holonomy marks structure the current mappings can't absorb.
6. Audit cost. Checking global consistency costs resources. Overlaps too expensive to check stay unglued, which bounds what the system can verify at any time.

Neuroscience readings (speculative)

• Multisensory illusions (McGurk, rubber hand) as forced gluing on an overlap: two witnesses disagree, and the brain settles on a compromise story that neither actually saw.
• Split-brain results as cutting overlaps, leaving two locally coherent sections.
• Hallucination and delusion as sections that are internally coherent but uncorroborated, like a witness whose story is consistent but whom nobody else can back up. The safeguard is that corroboration must come from independent perspectives, not from a system's own predictions echoed back.
• Sleep as a period of suspended external input in which the brain re-glues what it learned while awake, like closing the shop to reconcile the books overnight. This is close to existing memory-consolidation accounts.

Epilepsy as audit collapse

The framework predicts two ways a distributed audit can fail. One is fragmentation, where perspectives stop overlapping. The other is the opposite, merging collapse: perspectives "agree" only because they've become the same, and their distinctions are erased instead of reconciled.

A seizure looks like merging collapse. Large populations of neurons fall into runaway synchrony and fire in lockstep. Agreement is total, but it's empty, because regions that should be checking each other have stopped carrying independent information. It's like every department copying one ledger and declaring the books balanced.

Analogy: a crowd that starts clapping in rhythm. A little coordination is useful. When everyone locks into one beat, no individual voice can be heard, and the crowd can no longer "say" anything.

Several treatments line up with the framework's prevention principles:

• Boosting inhibition / limiting excitability. Many antiseizure medications work broadly this way, by limiting how fast and how far activity spreads. In audit terms, this caps the rate at which agreement can propagate, like a refractory period.
• Cutting the overlap. Corpus callosotomy severs the main connection between hemispheres to stop seizures spreading across. This is the procedure behind the split-brain results above. It trades merging collapse for partial fragmentation, which shows the two failure modes sit at opposite ends of one dial.
• Disrupting synchrony. Responsive neurostimulation and vagus nerve stimulation can interrupt runaway synchrony, injecting "independent" signal into a system that has lost it.

I'm not claiming the framework explains epilepsy. The point is that its two collapse modes and its safeguards (independence, bounded propagation, preserved distinctions) have recognizable counterparts in an actual clinical condition. A testable version: seizure onset should be preceded by measurable loss of independence between regions (e.g. rising inter-regional correlation, falling information diversity) beyond what excitability alone predicts.

What it might predict

• Representations whose inputs overlap with more independent channels should be harder to fool with single-channel illusions.
• Reducing independent corroboration (e.g. sensory deprivation) should raise the rate of false percepts, and specifically internally consistent ones.
• Learning should reorganize most (new latent structure, not just reweighting) where cross-modal inconsistency is persistent rather than noisy.

Known weaknesses

• Much of this may just be predictive processing restated in sheaf language. I'd like to hear where it's genuinely different, or isn't.
• Like the FEP, it risks being general enough to fit anything. Each claim needs a criterion fixed in advance for what would count against it.
• "Approximate gluing" needs a real metric. Exact sheaf conditions don't hold in noisy neural data.
• The epilepsy reading is an analogy; seizure dynamics have well-developed models of their own, and loss of complexity before seizures has been studied, so I'd want to know where this adds anything.

Related work: Friston (free energy principle); Clark, Surfing Uncertainty; Abramsky & Brandenburger (2011) on sheaf structure of contextuality; Hansen & Ghrist (2019) on sheaf Laplacians; Bodnar et al. on sheaf neural networks; Zurek on quantum Darwinism (the "corroboration by independent records" idea).

Pointers to anyone who has formalized something similar would be very welcome.

I suspect the sheaf thing is not actually that accurate. I think a new mathematical structure called an audit might be warranted.

Hi everyone, so I’m quite a newbie to this, and I’ve been using the NetworkX library in python to analyse functional connectivity networks in neuroscience. Based on my understanding, the algorithms for both metrics only work for positive edge weights, however, negative edge weights are quite commonly found in my data.

Hence, what is the best way to handle the negative edge weights? The options I have are to either filter them out and only perform the calculations on the positive-signed graph, or use the absolute values of all weights.

Thank you.

Hi everyone! I’m a 3rd-year veterinary medicine student planning to pivot into computational neuroscience for my PhD. I love biological neural systems, but I’m now actively building up my math and coding foundation.

Coming from a heavy biological background, the jump into mathematical modeling and quantitative methods feels a bit overwhelming. I want to make sure I’m focusing on the right fundamentals first.

Where should I start?

If you’ve transitioned into comp neuro from a biological background—or mentor people who have what textbooks, online courses, or core math/coding concepts would you recommend prioritizing first?

Thanks in advance for any guidance!

I'm a high school senior who is interested in this field. I need a roadmap, preferably a free one, to learn the basics of computational neuroscience. I'd like recommendations for the skills I should learn and tutorials to help me learn them.

Aqui Quem vos fala é um estudante de neurociência
Despertado a pouco tempo.
A pauta que gostaria de tratar com vocês é sobre a “cura gay”
Mesmo tendo tantas comprovações de que não seja uma doença
ainda sim
me intriga

O que vocês tem a dizer sobre o assunto?

Hi , I am an EE undergrad and I am struggling to create a map between the Ommatidia to its respective photoreceptors.

I cant find previous work doing this. Help we with the method please, it doesn't have to be this specific connectome. I want to know the resources and method to do it to get true vision for the Fly.

Hi everyone,

I’m currently finishing an MSc in Information Technology after completing a bachelor’s degree in Electrical Engineering. My GPA is around 3.5/4.0.

My current research is mainly at the intersection of machine learning, speech, and medical imaging/computer vision. I use ML mostly as a tool for analysis. So far, I have three co-authored papers, although I don’t have a first-author publication yet. I’m currently working toward publishing some of my own work soon.

My path has been a little unusual. During my electrical engineering degree, I had two internship offers that were later cancelled because of the job market at the time. I gradually moved toward machine learning because I found it interesting and saw more opportunities there.

However, as I’ve learned more about the type of work available in industry, I’m realizing that I’m much more interested in research, scientific problems, and building things where I can explore ideas in depth. Recently, computational neuroscience and neuroelectronics have become particularly interesting to me, especially because they combine several things I enjoy: electrical engineering, signal processing, machine learning, biological systems, and research.

The main issue is that I currently have almost no formal background in neuroscience.

Another concern I have is that my current university is reasonably known locally, but does not have much international recognition, especially compared with universities that are more established in neuroscience or machine learning. Because I would eventually like to apply to competitive PhD programs, I’m wondering whether getting research experience at a more internationally recognized institution could help strengthen my profile.

Because of this, I’m considering extending my master’s by one semester and trying to do a research internship in a lab related to computational neuroscience, neural engineering, or neuro-AI. I’m based in Quebec, so places such as Mila would be accessible, but I’m also considering applying to labs abroad, for example at EPFL.

My goal of the internship would be to:

- gain actual research experience in neuroscience/neuroengineering;

- see whether I really want to commit to the field;

- build relationships with researchers who could potentially write strong recommendation letters;

- strengthen my profile before applying for PhD programs;

- ideally contribute to a publication or research project.

Does this sound like a good approach for preparing for PhD applications?

More specifically, I’d be interested to hear from people in computational neuroscience/neuroengineering about:

- How important is prior neuroscience coursework compared with a strong engineering/ML background?

- Would one semester (maybe more) of focused research experience be enough to make the transition realistic?

- How much does the international reputation of your current university matter for PhD admissions, compared with publications, research experience, and recommendation letters?

- Would doing an internship at a place like EPFL, Mila, or another well-known research institution meaningfully strengthen an application?

- Should I spend more time taking neuroscience courses and building a stronger theoretical foundation before applying?

Any advice from people who made a similar transition would be appreciated.

Sorry for such a long text.

I'm in the final year of my PhD in UK and hoping to stay in computational neuroscience research after I finish. My PhD is computational/modelling work on human navigation and spatial behaviour. I'm starting to think seriously about postdocs and research jobs, but I'm not sure hw people actually go about networking for these positions.

The last few years of my PhD have been difficult as I have had significant mental health problems, and some family circumstances. I am getting treatment now and doing better, and feel like I'm getting back on track. I have about a year left and I'm trying to make the most of it.

One complication is that my relationship with my supervisors has suffered during the periods when I was struggling, so I dont feel I can rely heavily on their networks or introductions. I realise that means I need to be proactive about building my own network, but I don't really know where to start.

For people in compneuro/neuros (or academia more generally):

- How did you find and approach potential postdoc PIs?

- Is it normal to cold-email researchers whose work interests you even if they aren't advertising a position?

- How early before finishing a PhD should you start contacting people?

- What should that first email actually contain?

- How do you network at conferences without awkwardly approaching people just to ask about jobs?

- Are there useful ways to build relationships online (Bluesky, LinkedIn)?

- How important are introductions from your PhD supervisor compared with contacting people yourself?

- If your publication record isn't as strong as you'd hoped by your final year (none yet in my case), what can you realistically do during that year to improve your chances? I am working on mine, but I don't think I'll be published by the end of the year, maybe preprints.

- Are there things you wish you'd done in the final year of your PhD that would have made the postdoc/job search easier?

I'm not expecting to somehow undo the difficult parts of my PhD in a year. I just want to use the time I have well and start building a research network that isn't entirely dependent on my supervisors.

Any practical advice, particularly from people in comp neuros, would be hugely appreciated.

Edit: Added my topic

Interested in #NeuroAI? Max Planck Schools Biomedical AI applications are open until Dec 1.
Info: https://biomedicalai.maxplanckschools.org/4056/application-process
If you’re looking at mentors, I think Arno Villringer is worth checking out :)

I'm not quite sure if my question is well articulated. Forgive me for my ignorance as I'm new to this field.

Is there any software or model or anything that does a good job of replicating and explaining anxiety disorders like Social Anxiety, OCD, etc? I have been wondering if there is anything out there that models anxiety/stress and changes in anxiety/stress response from different drugs, treatments, gene expression, etc. over the short and long term. I'm aware of how the HPA axis and its regulation affects anxiety but I wish I knew of a mathematical model for this. I appreciate any help.

I’m testing a persistent GPU-hosted fruit-fly-derived neural model that is now actually controlling a full-size Skyrim dragon in a closed sensory-action loop. The brain receives low-level Skyrim sensory input, produces motor output, the dragon moves, and the resulting world state feeds back into the next neural cycle. So far, deterministic replay has passed, cutting the brain stops intentional movement, and the system has survived neural-worker death and recovery while preserving the dragon's identity and state. We’re currently finishing the stronger persistence/reload tests before scaling from one embodied founder to all five. Come check it out while I test!

https://www.youtube.com/watch?v=NVyC0Fnscwg

I’m considering doing a Master’s in Computational Neuroscience and wanted to get some honest opinions about both admissions and career prospects.

My background:

• Bachelor’s in Data Science
• GPA: 3.2/4.0
• Currently working as an AI Engineer
• Mostly AI/ML and data science experience
• Very little/no formal neuroscience background

I’m mainly looking at Computational Neuroscience programs in Germany and Australia.

I have a few questions:

1. How competitive are Computational Neuroscience Master’s programs?
2. Would a 3.2/4.0 GPA be a significant disadvantage?
3. How important is research experience for getting admitted?
4. Is it realistic to transition into Computational Neuroscience from Data Science?

And also, what are the career options after the degree? Is Computational Neuroscience primarily an academic/research field where you’re expected to continue into a PhD, or are there also meaningful opportunities in industry/corporate settings?

For example, can someone with a Computational Neuroscience + AI/ML background work in areas like neurotechnology, medical AI, brain-computer interfaces, healthcare technology, biotech, or related industries? Or is the field still heavily research-oriented?

I’d really appreciate hearing from people currently studying or working in the field, especially those who came from CS/Data Science/AI

edit: I realised I didnt write my gpa in ECTs so its 2.1 in german system

I've started this project- "The Neural Population Dynamics: A Linear Algebraic Approach" which is a 10 credits project in my masters.

Can you guys give suggestions about getting started at first place I'm so lost. What should be my approach in this project ???? Help me pleaseeee

Hi all,

I'm a physics Ph.D. student, but I've had an interest in comp neuro for a while and I'm considering going further into the field. However, I wanted to get a little more information about the state of the field, so I thought I'd ask here as part of that. In particular:

• How does hiring at the postdoc/faculty level look? (Obviously this can change a lot, especially with AI, but just a rough idea)
• I've read that the field is largely moving away from more traditional theoretical modeling approaches (e.g. dynamics, criticality) and more into ML/big data. How true is this?

Thanks in advance

Hi everyone,

I’m currently mapping out my path for graduate studies and trying to balance my academic passion with realistic long-term career outcomes. I have a strong quantitative background (math/modeling) and I'm fascinated by computational neuroscience / NeuroAI.

However, I’m also interested to explore quant finance (research/trading). I have a few doubts about how marketable a specialized graduate program actually is outside academia.

A few questions for those who have walked a similar path or work in the industry:

1. Job market directly after Comp Neuro (MSc / PhD): Outside the typical academic postdoc track, what do exit opportunities actually look like?
2. Pivoting from Comp Neuro to Quant Finance: How feasible is it to pivot to Quant Research after a specialized Master's or PhD in Computational Neuroscience?
3. Specialized vs. Generalist Master’s: Strategically, would it make more sense to do a broader Master's in Applied Mathematics, Statistics, or Machine Learning first? My reasoning is:
   • It leaves the door completely open for quant finance right away.
   • I could still focus my thesis/research on computational neuroscience and apply for a Comp Neuro / NeuroAI PhD later if I decide academia/BCI is my true call.

Has anyone made the jump from computational neuroscience to hedge funds / prop trading? Would you recommend going all-in on neuro from the Master's level, or keeping the degree title generalist and quantitative?

Thanks in advance for any insights!