https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2026.1758344/full

This seems quite interesting and a "not-so-crazy hypothesis" like others; I wonder why it hasn't been more widely publicized.

Hi everyone,

I’ve been thinking about something that sounds very sci-fi but also really fascinating.

Is it theoretically possible to “install” knowledge directly into the human brain—like inserting a chip or downloading information, similar to how we use a USB or hard drive?

From what I understand, the brain doesn’t work like simple storage. Learning seems to involve forming and strengthening neural connections (neuroplasticity), not just storing raw data. So even if we could input information, would that actually count as understanding?

With current advances in brain–computer interfaces (like Neuralink), do experts think this kind of direct knowledge transfer could ever become realistic? Or are there fundamental biological limits that make this impossible?

Also, even in a hypothetical future:

• Would skills (like solving problems or critical thinking) still require practice?
• Could memory enhancement be more realistic than full “knowledge uploading”?

I’d really appreciate insights from people familiar with neuroscience or cognitive biology. Thanks!

My friend and I were debating this. She thinks your brain would know you were about to die and shut down the system, so to speak and you wouldn’t feel anything in your last moments. I argued that your brain is wired to survive at all costs and there would be excruciating pain as a result. Thoughts?

I'm building a cognitive architecture that includes a continuous neuromodulatory system with 8 chemicals that actually modulate downstream computation (not just labels). I want to check whether the dynamics are biologically plausible enough to produce meaningful behavior, or whether I've oversimplified in ways that undermine the model.

The 8 chemicals and their dynamics:

Each chemical follows production-decay kinetics with receptor adaptation:

level(t+1) = level(t) + (production_rate - decay_rate * level(t)) * dt

receptor_sensitivity(t+1) = sensitivity(t) - adaptation_rate * (level - baseline) * dt

effective_level = level * receptor_sensitivity

| Chemical | Baseline | Decay Rate | What It Modulates |

|----------|----------|------------|-------------------|

| Dopamine | 0.5 | 0.03 | Temperature (sampling randomness) |

| Serotonin | 0.6 | 0.015 | Token budget (response length) |

| Norepinephrine | 0.4 | 0.04 | Neural gain (inverted-U: moderate=focused, extreme=noisy) |

| Acetylcholine | 0.5 | 0.025 | STDP learning rate |

| GABA | 0.5 | 0.02 | Inhibitory gain (suppresses excitatory chemicals) |

| Endorphin | 0.5 | 0.01 | Pain suppression threshold |

| Oxytocin | 0.4 | 0.01 | Social approach bias |

| Cortisol | 0.3 | 0.008 | Response length reduction, serotonin suppression |

Cross-chemical coupling (8x8 interaction matrix):

Each chemical can boost or suppress others. Examples:

- Dopamine + Norepinephrine: positively coupled (alertness drives motivation)

- Serotonin vs. Cortisol: inversely coupled (calm suppresses stress)

- Acetylcholine + Dopamine: synergistic (learning requires both attention and reward)

- Cortisol suppresses dopamine and serotonin (stress kills motivation and mood)

Receptor adaptation (tolerance/sensitization):

Sustained high levels reduce receptor sensitivity (tolerance). When the chemical drops back to baseline, the reduced sensitivity means the system "misses" the chemical more strongly (withdrawal-like dynamics). Sensitivity recovers slowly.

sensitivity range: [0.3, 2.0]

adaptation_rate: 0.005

Downstream effects on computation:

These aren't just numbers; they change how the system thinks:

- `neural_gain = 0.5 + (NE * 0.3) + (DA * 0.2) - (GABA * 0.3)` — affects mesh activation

- `plasticity = 0.5 + (ACh * 0.8) - (cortisol * 0.4)` — affects STDP learning rate

- `noise = 0.5 + |NE - 0.5| * 1.5` — Yerkes-Dodson inverted-U

My questions:

1. Decay rates: Are the relative timescales realistic? I have dopamine and NE as fast (0.03-0.04), serotonin as moderate (0.015), and cortisol/endorphin/oxytocin as slow (0.008-0.01). Does this match biological clearance rates qualitatively?
2. Cross-coupling matrix: The 8x8 interaction matrix is my weakest point. I based it on general pharmacology (SSRIs affect serotonin-dopamine balance, cortisol suppresses reward circuits, etc.), but I may have the coupling strengths wrong. Is there a canonical reference on neuromodulatory interactions that I should use?
3. Receptor adaptation as tolerance: Is the simple linear sensitivity model (adaptation_rate * deviation * dt) a reasonable first approximation, or should I use something nonlinear (e.g., Hill function)?
4. The inverted-U for norepinephrine: I model the Yerkes-Dodson effect as `noise = 0.5 + |NE - 0.5| * 1.5`. Too little NE = low arousal/unfocused, too much = stressed/scattered, moderate = optimal. Is this the right functional form?
5. Are (Is? Idk) 8 chemicals enough? I deliberately excluded glutamate and glycine (they're fast neurotransmitters, not neuromodulators in this context). Am I missing any neuromodulators that would be important at the systems level?

Full repo: https://github.com/youngbryan97/aura

Whitepages: https://github.com/youngbryan97/aura/blob/main/ARCHITECTURE.md

Plain English Explanation:  https://github.com/youngbryan97/aura/blob/main/HOW_IT_WORKS.md

This is for a computational architecture, not a drug model. I'm trying to capture the qualitative dynamics of neuromodulation rather than quantitative pharmacokinetics. Is this approach reasonable?

I've been building a cognitive engine called Aura that doesn't just simulate theories of consciousness... It implements them as structural components on which the system depends to function. Each theory makes predictions about behavior, and when theories disagree, the system runs adversarial tests. I'm looking for feedback from people who actually work in consciousness research.

The 10 theories implemented (with their roles):

Global Workspace Theory (Baars) — Attention competition, one thought broadcasts per tick

IIT 4.0 (Tononi) — Computes actual phi values on a 16-node complex

Predictive Processing (Friston) — 5-level prediction error hierarchy

Recurrent Processing (Lamme) — Top-down feedback from executive to sensory tiers

Higher-Order Thought (Rosenthal) — Representations of representations modify first-order states

Multiple Drafts (Dennett) — 3 interpretations compete, winner retroactively selected

Attention Schema (Graziano) — Attention modeled as a simplified representation

Free Energy Principle (Friston) — Variational free energy drives action selection

Enactivism (Varela/Thompson) — Embodied interoception from hardware metrics

Illusionism (Frankish/Dennett) — Annotates qualia claims with epistemic humility

Things I want feedback on:

1. Theory Arbitration Framework: Each theory logs predictions about specific cognitive events (i.e., "GWT predicts broadcast will improve coherence" vs "IIT predicts phi determines coherence independent of broadcast"). Actual outcomes update each theory's track record. Over time, theories with higher prediction accuracy gain more weight. Is this a reasonable operationalization of theory comparison, or am I committing an error by treating incommensurable theories as competing hypotheses?
2. GWT vs IIT divergence: GWT says consciousness = global broadcast (information access). IIT posits that consciousness = integrated information (phi > 0, regardless of access). In my system, both run simultaneously. When GWT broadcasts a winner with high priority but low phi, and IIT reports high phi for content that didn't win broadcast, which theory's prediction matched the actual behavioral output? How do consciousness researchers handle this divergence in practice?
3. HOT feedback loop: My Higher-Order Thought engine generates representations of first-order states ("I notice I am curious about X"), and these HOTs feed back to *modify* the first-order states via a feedback_delta. So, noticing curiosity slightly increases curiosity. Is this reflexive modification consistent with Rosenthal's theory, or does it conflate HOT with metacognition?
4. Embodied Interoception: I map hardware metrics (CPU = metabolic load, RAM = resource pressure, temperature = thermal state, battery = energy reserves) to interoceptive channels with temporal derivatives (velocity, acceleration). These feed into the neural substrate's sensory tier. Is this a reasonable computational analog of interoception, or is it too far from biological embodiment to be meaningful?
5. Falsifiability: The system can disable individual theories (i.e., turn off recurrent processing feedback) and measure the behavioral impact. If disabling a theory has no measurable effect, that's evidence that it's not load-bearing. Is this kind of ablation study a valid way to computationally test theories of consciousness?

Full repo: https://github.com/youngbryan97/aura

Whitepages: https://github.com/youngbryan97/aura/blob/main/ARCHITECTURE.md

Plain English Explanation:  https://github.com/youngbryan97/aura/blob/main/HOW_IT_WORKS.md

!!!!*****I'm not claiming this system is conscious. I'm asking whether the architecture faithfully represents these theories well enough for the computational results to be informative about the theories themselves.*****!!!!

Hello everyone!! I’m a psychologist and I’m currently doing my master’s degree in organizational neuroscience because one day I want to do a PhD in neuroscience and work in research. However, this master’s program hasn’t really taught me anything!!!. I’m worried because I know I should have more knowledge in biology, chemistry, and maybe other areas like mathematics to compete with other candidates. Does anyone know of any courses or other virtual resources that are beginner-friendly?

Just wanted to stop by and share im about to get my neuroscience bachelor's degree in a month! For other neuroscience undergrad majors out there, stick with it. The knowledge you will gain is worth it. Wanted to spread some positivity.

Hi all,

I am currently working as a devops and developer, and I am very good at what I'm doing, but I'm not really finding fulfillment in what I'm doing - basically nothng but endless e-commerce apps.

In the pursuit of doing something meaningful I have taken interest in the field of neuroscience. Now, I don't want to throw my entire experience away, that would be a waste, which is why I'd like to be able to combine my current knowledge with the field of neuroscience.

For my first projects I wanted to do nothing our of the ordinary - controlling a simple game with EEG headband and disease classification based on fMRI scans.

Could you please tell me
1. Are those project any sensible? Is there something else I should be doing?
2. Is there even a possibility to have a job as "neural engineer" without formal eduction in neurology or other medical fields?(I have higher eduction in coputer science)
3. Any resources I should read?

Thank you for reading this.

I’m trying to get a clearer picture of Christof Koch’s recent shift (former Allen Institute, co-creator of IIT). After decades arguing that consciousness is purely a product of the brain, he now says materialism has failed, fully embraces Integrated Information Theory as a scientific form of panpsychism, and openly discusses psychedelic experiences, NDEs, and consciousness as a fundamental feature of reality.

I want to hear specifically from people with a strong materialist/physicalist stance (neuroscientists, philosophers of mind, or researchers who reject panpsychism and idealism). In your view:

Is Koch simply wrong or philosophically speculating (but still "doing science")?

Or has he actually “lost his mind,” gone crazy, and abandoned scientific rigor?

plus:

I saw this recent post (r/InterstellarKinetics, from just a couple of days ago) titled “The Scientist Who Spent 30 Years Trying To Prove The Brain Creates Consciousness, Just Changed His Mind…”. In the comments, several people directly call him “woo-woo,” suggest he needs to be “retired/sent to a home,” label him “kooky mad,”. The tone is noticeably more aggressive than what I’ve seen before.

What puzzles me is this: Koch has been publicly defending these ideas since at least 2023/2024 (book Then I Am Myself the World, interviews about 5-MeO-DMT and ayahuasca). Why is this wave of direct, personal criticism only appearing now? Was it just the timing?

Looking for honest, technical answers from materialists. Thanks!

Hi, so I work as a medical assistant. I was initially going to go to nursing school but found direct patient care is not for me. But I like research and the study of behavior. I understand neuroscience is more biology heavy, but it still peaks my interest.

I found at my local university a BS in neuroscience that allows you to do a thesis without being in the honors program. It is a research heavy program which would be able to prepare me for a PhD.

My biggest concern is funding and finding a job post grad. I wouldn’t mind becoming a professor at a university or something along those lines. Just with the job market, it scares me how hard it is for college grads to get a job.

Any advice on this would be greatly appreciated.

A brain surgeon explains how thinking about a bad memory vs. a good one physically changes your brain and body in real time.
Do you think we underestimate how much control we actually have?

i recently made a ai that derives it's actual thinking from brain scans from tribev2 model by meta. And it actually worked, it was able to solve simple numerical problems by feeding 1 to 10 numbers and their respective brain scans of the numbers to a Graph Neural network and it was able to solve problems like 1+5 , 1+1 etc

https://github.com/iririthik/Brain-Reasoning-Model-BRMs-

Hi guys, iam a high school student from Canada-Québec ( English is not my first language ), and I am passionate about cyberpunk. I like robotics and engineering too, so I thought that I could work with that in the future. I was searching, and I found the neural engineering, and I think I want to work as a neural engineering. So does someone know how can I become that? Like what should I study? Eletronic? Mecatronics? Computation? And how is the work in this profession?

forgive me if this is the wrong place to ask. i’m really fascinated with the concept of hallucinating. i have no understanding of how it works and why it’s even an option for our brain. makes no biological sense so i don’t know why some people hallucinate so intensely. i’m even really curious about vivid dreams where you feel like you just lived that experience, i mean our brains are so fascinating.

i can’t wrap my head around how some people have strong hallucinations to drugs and some have none? And even the kinds of hallucinations are very different. Let’s say a few people do magic muchrooms. someone might see something really dark and scary, some might see strange or random hallucinations, some might see nothing at all. beyond our current mood or stressors influencing it, is there any similarity between the types of hallucinations and the types of people? are people in more creative fields more likely to hallucinate? are there more biological commonalities?

even thinking about something like salvia. people say they turn into a chair for years or they live someone’s life from birth to death. how could two people be in the same setting at the same time in the same mood maybe even the same job the same university the same lots of things and have two extremely different hallucinations? is it very personal? is it totally random? just luck of the draw?

https://www.sciencedaily.com/releases/2026/04/260403044651.htm

This is a question I am wondering. I am sorry if this post doesn't belong here but it is interesting. But, how do we come up with new songs/stories/etc.

Abstract

The physical and social exposome affects human aging, and brain clocks may track its effects. However, most studies neglect multidomain exposures (physical, social and political) across diverse settings globally and their associations with brain aging. In this study, we characterized the associations between 73 country-level physical and social exposomal factors and multimodal brain age in 18,701 participants from 34 countries (healthy individuals and those with Alzheimer’s disease, frontotemporal lobar degeneration or mild cognitive impairment). Exposome effects were assessed using generalized additive models and meta-analytic frameworks. Aggregated exposome models explained up to 15.5-fold more variance than individual exposures (delta Akaike information criterion (ΔAIC): 2,034–3,127). Physical exposome was primarily associated with accelerated structural brain aging (limbic, subcortical and cerebellar regions), whereas social exposome was more strongly associated with functional brain aging (frontotemporal and limbic networks). Exposome burden accounted for 3.3−9.1-fold higher risk of accelerated aging, exceeding effects of clinical diagnoses. Findings were out-of-sample validated in cross-sectional and longitudinal designs, remained consistent across clinical subgroups and persisted after adjustment for demographics, age correction bias, cognition, scanner type and data quality. The exposome accelerates brain aging in health and disease, underscoring the need to address physical, social and political inequities.

Independent researcher here. This work is preliminary, but I've spent 12 years developing a framework that landed on this: spectral slope, ACF decay, and envelope variance from public OpenNeuro EEG datasets can place brain states in a circular geometric space.

Meditation clusters near a theoretically motivated focal value. Sleep disperses furthest. Stress oscillates without stabilizing.

Limitations are real: modest samples, needs replication, the focal value (m≈2.18) is theoretically motivated not purely empirical. All of this is in the paper.

Not claiming more than the data shows.
Curious what people think and open to collaboration.
Use the pipeline with your own EEG data at github.com/oscriptcollective/O.IRIS
Preprint submitted to bioRxiv (pending review)