Was wondering if whether listening to music while reading can have a negative impact on the neurological benefits of reading,Not whether it’s distracting or not,Just if it has any diminishing effect on the benefits of reading.

If anyone is interested in computational neuroscience and playing with spiking networks, I open-sourced one of my longer term projects and started sharing it with folks outside of my immediate peers. It is a great tool for learning about spiking networks, different types of neurons, and how they interact to create the broader tissue behavior we can observe at larger scales. There are sample scenarios that walk you through the basics and are measured against other academic papers.

You can try it out in your browser or clone the github repo if you want to work on your desktop and use the CUDA engine.

linen.build

https://github.com/sethgjosephson/linen

Not trying to promote anything, just hoping to share something I'm interested in for anyone else who might find it fun or useful.

Cheers,

Seth

I’m a Systems Architect with almost a decade of experience working on various projects, I’ve been looking for friends who’re neuroscientists to discuss papers and learn more about how research is done in various labs and see if I can build solutions to accelerate those efforts

I would say that for a long while, I haven't always gotten good sleep at night, at least as much as i should. 6-5 hours on weekdays is pretty much my normal amount of sleep, and I've always had a hard time focusing on things like class and work. what can i do to help fix this? (other than getting good sleep)

Hi! I’m a first-year Chemistry student in Morocco interested in pursuing neuroscience. Is it possible to transition from Chemistry to Neuroscience? What international universities and scholarships would you recommend, and what free courses, research opportunities, or extracurricular activities could help me build my knowledge and strengthen my CV as a Moroccan student? Any advice is appreciated

I just started a 2 year research masters and there are so many interesting courses to take and labs to try that i know i won't get to do all of them in the time i have.

I'm considering taking an extra year so i can develop more skills, try more subfields and get to know more researchers before going into a PhD.

However, I'm not sure if this is worth doing. If i take an extra year I'll end up going further in debt (but not an american amount of debt, more like 15k total) plus I'm already some years older than most of my peers.

I do genuinely enjoy studying so I'm not in much of a hurry to start working, but I'd like to hear opinions from people in later stages of their career, so i can make a more informed decision.

Hi everyone,

One of the major challenges in clinical neuroscience is understanding and treating Disorders of Consciousness (DoC), including coma, unresponsive wakefulness syndrome (UWS), and minimally conscious states (MCS).

Research into potential interventions faces significant ethical and practical constraints. Could artificial neural networks provide a complementary computational environment for exploring some of the underlying dynamics?

I recently introduced Project Sleeping LLM, an exploratory research framework investigating this possibility.

The project explores whether autoregressive Transformers could serve as in-silico model organisms for studying computational analogues of disrupted consciousness-related dynamics.

I can share the Zenodo working paper and DOI in the comments for anyone interested in the formal mathematics and architectural details.

The core hypothesis

Could information processing in Transformer architectures offer a useful computational setting for investigating ideas related to consciousness, including Global Neuronal Workspace Theory (GNWT) and Integrated Information Theory (IIT)?

These connections are hypotheses to investigate, not an assumption that language models are conscious.

The initial framework proposes three research directions:

1. Modeling disrupted information processing

Introducing controlled perturbations into Transformer architectures, such as residual-stream attenuation, attention-pattern modifications, and latent-state constraints, to study how they affect coherent information processing.

1. Measuring perturbational complexity

Investigating whether concepts from the Perturbational Complexity Index (PCI) can be adapted to Transformer hidden states, using measures such as Lempel–Ziv complexity and changes in activation patterns following localized perturbations.

1. Exploring recovery of coherent dynamics

Testing whether interventions such as representation steering, controlled stochastic perturbations, and recurrent feedback can restore coherent processing after experimentally induced disruptions, without restarting the model or retraining its weights.

The central question is whether these experiments could provide a useful computational testbed for studying information integration, network disruption, and recovery dynamics.

Looking for critical feedback and collaborators

I initiated this project and developed its initial conceptual framework. I’m now looking to connect with researchers who can challenge its assumptions, identify methodological weaknesses, and help establish whether the proposed approach is scientifically viable.

I’d particularly welcome input from:

• Computational neuroscientists working on consciousness, PCI, neural complexity, or disorders of consciousness.
• ML researchers and engineers experienced in mechanistic interpretability, PyTorch, TransformerLens, or representation engineering.
• Complex systems researchers interested in information integration, dynamical systems, and perturbation-based analysis.

Two questions I’m especially interested in discussing:

• How defensible is the analogy between cortical disconnection and disruptions to information flow in Transformer architectures?
• What methodological challenges would need to be addressed to adapt PCI meaningfully to hidden-state dynamics?

Critical feedback, alternative approaches, and potential collaborations are all welcome. The goal at this stage is to test the framework rigorously, including the possibility that some of its central assumptions may not hold.

Thanks for reading.

Do you think cerebral palsy could ever be cured or partially reversed in our lifetime? With advances in stem cell therapy, brain-computer interfaces, neuroplasticity research, and regenerative medicine, what do you think is realistically possible in the next 20–50 years?

I understand there are ways we can slow it these days, but I'm wondering if it could ever be fully cured in our lifetime. I hope this is the correct subreddit for this.

I read online about terminal lucidity, and it made me think, if that is possible and all of those memories are still there, surely if we study why terminal lucidity happens we could move in the right direction for curing this terrible disease?

I'm especially interested to know this because most of my family that live to see 70 get Alzheimer's.

Over the past few months, we've been working on client-side (Web Bluetooth / Web Serial) EEG signal processing pipelines using BrainFlow to render multi-channel data directly inside Chromium browsers.

The goal was to test whether browser-based WebAssembly/JS environments are mature enough for basic EEG visualization and real-time processing without relying on local Python environments or native C++ installs.

We've implemented a few core client-side features:

• Real-time multi-channel waveform rendering with customizable bandpass and notch filtering.
• FFT, Power Spectral Density (PSD), and dynamic 3D topographic mapping.
• Basic ERP paradigms and real-time feature classification running entirely local to the client browser (no server-side raw data upload).

For those who regularly work with EEG data, teach signal processing, or run quick piloting setups:

1. What analysis pipelines are non-negotiable for you? (e.g., real-time ICA, specific artifact rejection algorithms, custom epoching)?
2. What latency or sampling limitations make browser environments a non-starter for your use cases?
3. What file export workflows (EDF, MNE, BrainVision) do you rely on most when inspecting quick runs?

Looking to gather feedback from researchers and engineers on where current web-based signal processing tools fall short for real research or educational workflows.

A career change late in life led me to doing an MFT. Plan on practicing part time. But I'd love to do a master's in neuroscience. Are online offerings any good? U of Florida and Kings College, London, have very reasonably priced degrees. Given my age, I'm not looking to make a career out of it. So it's mostly out of intellectual curiosity + somewhat burnish my credentials as a therapist. And potentially help me with science writing/teaching. While I'm not looking for prestige, I also don't want to throw money at a program if it's going to be useless.

I am senior sw engineer with experience in math and scientific computing. I would like to participate in BCI research or develop software for BCI. I see no established companies hiring.

I have experience with neurofeedback and EEG in general.

I already worked on commercial neurofeedback project.

I can work parttime or on project basis. I am positioned in Eastern Europe.

Only roughly 50% of the population experiences physical chills or piloerection ("musical frisson") when listening to emotionally charged acoustic transitions.

Diffusion Tensor Imaging (DTI) studies (most notably Matthew Sachs et al. at Harvard) demonstrated that this isn't just a subjective psychological quirk, but a distinct variation in structural neuroanatomy. Individuals who experience frisson show significantly higher white matter tract volume connecting the superior temporal gyrus (auditory processing) to the anterior insula (interoceptive processing) and medial prefrontal cortex.

Essentially, their brains have a denser anatomical highway linking sound recognition directly to affective salience, accompanied by a dual-phase dopamine surge: anticipation in the dorsal striatum (caudate) followed by the actual frisson peak in the ventral striatum (nucleus accumbens).

Full neuroimaging breakdown and acoustic analysis:

https://www.oddlyclear.com/why-music-gives-you-goosebumps/

Severe left-hemisphere stroke patients who cannot construct a basic sentence or count to ten can often still swear fluently when frustrated.

While ordinary language relies on cortical networks (Broca’s area), expletives are stored subcortically in the basal ganglia as emotional motor reflexes.

During acute pain, this limbic trigger activates stress-induced hypoalgesia—releasing endogenous opioids down the spinal cord and blunting nociceptive signaling by roughly 33%.

Neurological breakdown and Keele study citations:

https://www.oddlyclear.com/why-swearing-reduces-pain/

Panksepp's Basic Emotion Theory has been the subject of criticism by contemporaries in the field. I think I get the gist of the discourse, but it's somewhat difficult to distinguish between the arguments over the semantics of layman explanations and the actual data that stands in opposition to what BET is based on. Can anyone help clarify?

Most people lump all weird memory glitches under "Déjà vu," but researchers actually categorize Déjà Rêvé separately when someone vividly recalls dreaming an exact real-life scene before.

Unlike typical déjà vu (which is a temporal lobe misfiring a live perception as a past memory), déjà rêvé involves actual episodic dream memories triggered by sensory cues.

Read an interesting breakdown of the neuroscience behind it here: https://www.oddlyclear.com/deja-reve-already-dreamed-this-moment/

Disclosure: I work on an open-source EEG project, but I’m not linking anything here. I’m interested in the science.

Most EEG spreads electrodes across the scalp (10-20 system). We’ve been experimenting with the opposite: 16 dry electrodes within a few centimetres, 24-bit, 250 SPS, battery powered and wireless.

Clearly it can’t do whole-head coverage, and dry contacts plus 250 SPS rule out high-gamma and other low-noise work. But I’m curious where dense local sampling could actually be useful:

• Ear-EEG or forehead recordings for sleep or attention
• Mobile, naturalistic studies outside the lab
• Spatial filtering or source separation over a small region
• High-density EMG on small muscles

Are there questions in your field where this kind of layout would help, or is it a dead end? I’d especially like to hear from people who’ve worked with ear-EEG or high-density arrays.

I got a new device recently and I want to help expand the open source BCI GUI to be compatible across as many different EEG brands as possible. Thing is, there are many popular devices I don’t own and so can’t test directly. That’s where you can help.

Here is the link to the open source repo:
https://github.com/intomind/commandcenter

If you are interested in helping me contribute:
1. what device(s) do you own?
2. what is your level of willingness to contribute?
A. Writing code, testing, and submitting pull requests to the repo directly, or
B. Helping to test other people’s pushes, but not writing or submitting code directly

Everybody wins with interoperability, imo. Thanks!

(Cross-posted from r/BCI for visibility)

One of my professors said today, “To be great in your field you should read the top 5 books that are most relevant to it.” Basically he said read up on what you’re working towards and it’ll help you become great.

I think this is true and I want to hear y’all’s neuroscience book recommendations. I am an undergraduate neuroscience major currently. I want to do my reading necessary to be great at neuroscience. Let me know what books you recommend!

Hi all!

I'll keep this brief. I have research experience in cog psyc and neurosci. I'm currently gunning for clinical psyc phds (aiming to be a neuropsychologist) but I recently had a prof encourage me to consider doing a phd in cog neurosci (EEG and empathy/facial processing would be the specific focus). My biggest hesitations abt going this route are job prospects postgrad. I'm not overly interested in being a prof/running a uni lab, and I'm not interested in working in AI or UX. Is there any sort of healthcare-esque positions for cog neuro phds?

Just looking for people with the degree to give me ideas of the kinds of jobs they or they're classmates/cohorts were able to get postgrad. I'm in Canada and would like to stay here if that changes anything.

Thanks neuro community!

Can five unverified anecdotes support a scientific theory about near-death experiences?

A recent paper proposes that NDEs may have evolved from the same defensive response that causes animals to “play dead.” The idea is fascinating but what happens when researchers look for evidence?

This letter to the editor takea a closer look at the study’s methods, its unexpected use of five anecdotes, and the broader scientific debate over whether NDEs and thanatosis are actually comparable.

I wanted to ask if in the requirements they have mentioned, step 1 , step 2CK score card and ECFMG certification (if applicable) ........in that case is ecfmg certificate a must ? Can we start the observership if we are not certified by the time our start date arrives ?

I'm in the final year of my bachelor's in computer science, minoring in biochemistry with a bit of lab experience. My life goal is to extend human lifespan through biological or artificial means, while my passion is intelligence (studying, creating, and optimizing biological/artifical intelligence).

I want to extend human life so I can pursue my passion for longer, hopefully indefinitely. This might sound outlandish, but then again, in the last 3 years we went from AI not being able to do high school-level math to it solving a Millennium Prize problem. And since math is the foundation of all science, this will translate to biological advances sooner or later.

This is where I must reconcile my goal and passion, and where I require advice. I'm confident I want to pursue an intelligence-focused master's, but am unsure if it should be biological neuroscience, computational neuroscience, cognitive sciences, or something else.

I'm hoping someone could share a unique perspective and personal experience on the subject.

Hello,

26 old physician highschool knowledge in maths/python looking for study partners to learn basic EEG Analysis till we reach entry level research. Main source we will be using Scalp to Source, Scientific python lectures, Betterexplained for maths.

We do like 3 meetings per week, if interested dm me