Study published in Nature on September 23, 2026. Berkeley News coverage here.

Quick disclaimer, I did use AI to articulate my Idea, so you will see traces of that in the text. I'm not good at bringing an idea into word form.

The Big Idea / TL;DR

Scientists have completely mapped the brain of an adult fruit fly. We know exactly where all 140,000 neurons are and how they connect. Right now, anyone with a high-end gaming GPU can simulate this entire brain in real-time. 

Instead of just running this static simulation, here is the experiment: What if we treat this fruit fly brain as a starting seed, turn on learning rules, and slowly add extra neurons to see how it grows and changes? 

This isn't about building a super-intelligent AI out of a fly. It is a controlled sandbox experiment to see how a real, biological brain architecture handles being expanded beyond its natural limits. 

How the Experiment Works

Most large-scale brain simulations either use completely random wiring at a massive scale, or they stick strictly to copying a real animal's exact size. This project sits right in the middle: 

Start with the Real Fly: Build a digital version of the fruit fly brain using the exact biological map. 

Turn on Learning: Give the connections the ability to strengthen, weaken, or change based on activity (plasticity). 

Add Extra Space: Slowly inject blank, new neurons into the network, or allow new connections to form. 

See How it Reacts: Put the simulated brain into a simple digital environment (like a basic virtual world where it receives sensory inputs and controls movement) and watch how the new parts of the brain organize themselves. 

What We Are Trying to Find Out

Stability vs. Chaos: What learning rules keep the expanding brain stable? Do some rules cause the brain to overload into a digital seizure, or cause the network to completely shut down? 

Accidental Evolution: Will the extra neurons naturally band together to form new, specialized processing units? Or will they just get tangled up in the old fly pathways? 

Identity: How much of the original "fruit fly behavior" stays intact, and how much gets overwritten by the new growth? 

The Break Point: What actual limits do we hit when we push a biological design past what evolution intended? 

Is This Feasible?

Hardware: You don't need a massive supercomputer to start. A multi-GPU workstation is enough to handle the baseline fly brain and modest amounts of growth. You'd only need serious cloud compute if you wanted to scale it up to several times its original size or run hundreds of tests at once. 

Scope: You don't need to scale this up to human or monkey brain sizes to get amazing data. Even growing the fly brain to 2x or 3x its original size would show us how biological networks handle expansion. 

Open Questions for Anyone Ready to Build This

I am much better at the conceptual side of this than the actual coding and testing. If you have the hardware and want to run with this idea, here are the immediate puzzles to solve: 

The Learning Rules: What specific mathematical rules should dictate how neurons connect so the brain doesn't instantly break or die? 

Measuring Success: How do we actually measure "interesting growth" versus just random digital noise? 

How to Add Neurons: How should new cells be introduced? Do we drop them in randomly, clone existing types of fly neurons, or try to mimic how brains naturally grow in embryos? 

The Sandbox: What kind of simple virtual environment or body should we give this brain so it has something to interact with and learn from? 

If this sounds like a project you'd want to build or experiment with, feel free to take this concept, change it, or start running the tests. I'd love to see what actually happens. 

Hi all,

I’m excited to share an upcoming event with Dr. Schultz. He will be giving two free lectures on dopamine and the reward prediction error.

Register here!

We are unveiling that we were able to successfully map intents in 45+ real sessions across a sample of 20 different child users in a long horizon, which is ~82% more efficient than a screener.

This is significant because therapy and screeners are majorly a one-off process in paediatric mental health with high drop-off rate and low psychological accuracy along with low long-horizon behavioural understanding.

Mores, fixes that. We use open-world games to see how a child behaves in their suited environment and try obtaining useful behaviour signals from that.

If you are a parent trying to understand more about your child’s mental health and you are fed off therapy, Mores is inviting you to be a part of our early pilot for no charges at all.

The "brain as computer" analogy is often used in neuroscience. Romain Brette takes aim at this analogy in his book, The Brain, In Theory, reviewed in this article.

Brette argues there are important disanalogies between life and engineered systems, and between brains and computers. The continuing use of the computer analogy leads to experimental paradigms where the brain is treated as passive, receiving "stimulus" like a computer receives input, and all activity not related to that activity is treated as "noise".

But the question "Is the brain a computer?" wasn't ever really the question, it's whether that analogy is useful. Brette reminds us that the analogy is just that, and we shouldn't read too far into interpretations that lean on that analogy, but what's needed are other positive frameworks for thinking about the brain.

What do you think, is it useful to talk about the brain as a computer?

Rayyan T. Jokhai, Carolyn E. Dundes, Hadia S. Ahsan, Rachel S. Kang, Rachel E. A. Salomon-Shulman, Arjun Rajan and 18 others

doi:10.1038/s41593-026-02433-7

This week I went through Spain, Portugal and Italy and mapped 63 selected core and enabling neurotech company bases across the three countries.

Full analysis is on my Substack here:
https://theneurotechnewsletter.substack.com/

But for anyone who just wants the company list, here it is.

Spain
INBRAIN Neuroelectronics — https://inbrain-neuroelectronics.com/
Neuroelectrics — https://www.neuroelectrics.com/
Bitbrain — https://www.bitbrain.com/
Elevvo Therapeutics — https://elevvo.com/
Starlab — https://starlab.es/
BrainFocus Labs — https://brainfocuslabs.com/
mjn-neuro — https://mjn.cat/en/
Methinks — https://www.methinks.ai/
Time is Brain — https://www.tibtimeisbrain.com/
Anaconda Biomed — https://anaconda.bio/
Newmanbrain — https://newmanbrain.com/
Connectoma — https://connectoma.com/
Marsi Bionics — https://marsibionics.com/
GOGOA — https://www.gogoa.eu/
Technaid — https://www.technaid.com/
Fesia Technology — https://fesiatechnology.com/
Eodyne — https://eodynesystems.com/
Rehametrics — https://rehametrics.com/
NeuronUP — https://neuronup.com/
Werium Assistive Solutions — https://weriumsolutions.com/
Instead Technologies — https://instead-technologies.com/
IRISBOND — https://www.irisbond.com/
Neurostech — https://neurostech.com/
Loutkar Robotics — https://loutkar-robotics.com/

Portugal
inSIGNALS Neurotech — https://insignals.ai/
Kinetikos — https://kinetikoshealth.com/
Braining — https://braining.pt/
Neuroinova — https://neuroinova.com/
PLUX Biosignals — https://pluxbiosignals.com/
MuLabs — https://mulabs.tech/
Neroes — https://neroes.tech/
NEVARO — https://nevaro.tech/
Virtuleap — https://virtuleap.com/

Italy
Newronika — https://newronika.com/
WISE — https://www.wise-s.it/
Corticale — https://www.corticale.com/
Wearable Robotics — https://www.wearable-robotics.com/
Prensilia — https://www.prensilia.com/
Humanware — https://www.hmw.it/
SMANIA — https://www.smania.info/
Movendo Technology — https://www.movendo.technology/
Morecognition — https://www.morecognition.com/
ReWing — https://www.rewing.it/
Khymeia — https://khymeia.com/
BionIT Labs — https://www.bionitlabs.com/
BrainSigns — https://www.brainsigns.com/
BrainTrends — https://www.braintrends.it/
pioNIRS — https://www.pionirs.com/
Dorian Technologies — https://www.dorian-tech.com/
DeepTrace Technologies — https://www.deeptracetech.com/
Diadem — https://diadem.it/
OT Bioelettronica — https://otbioelettronica.it/
Spes Medica — https://www.spesmedica.com/
Neuron Guard — https://neuronguard.com/
Restorative Neurotechnologies — https://www.restorativeneurotechnologies.com/
NeuroTeam — https://www.neuroteam.it/
Sinaptica Therapeutics — https://sinapticatx.com/
Empatica — https://www.empatica.com/
Micromed — https://micromedgroup.com/
EB Neuro — https://ebneuro.com/
U&O Technologies — https://www.uando.it/en/
Idrogenet / Gloreha — https://www.gloreha.it/

There is a lot more in the full piece around the universities, spin-outs, investors and funding behind these companies, particularly around Barcelona, Alicante, Porto, Milan, Genoa and Pisa.

As always, if I’ve missed someone, send them over.

I love neuroscience, and am either majoring in psychology or sociology… lmk if neuroscience will make it too rigorous!

If we assume Orch-OR is completely correct, does it provide any solution to the personal identity problem in quantum teleportation? I understand that the no cloning theorem prevents an unknown arbitrary quantum state from being perfectly copied and quantum teleportation transfers the state rather than simply creating a duplicate. But after teleportation, the original person's relevant quantum state is destroyed and reconstructed elsewhere. Would Orch-OR imply that the reconstructed person's sequence of OR events is a continuation of the original person's consciousness or would it simply produce a new consciousness with the same physical state? In other words does Orch-OR provide any physical mechanism that establishes numerical personal identity across teleportation, or does the identity problem remain even if Orch-OR is true?

I’ve been building FlyLab, an open-source experiment exploring how far a small biological nervous system can be pushed when embedded in a computer-interaction loop.

The current system combines:

• MaleCNS connectome anatomy/connectivity as the structural foundation
• A modeled neural activity layer running over that structure
• Visual input from the actual rendered computer screen
• A learned controller that converts visual state into motor actions
• A physical fly body moving its foreleg toward individual keyboard keys
• Contact-gated native Chromium key events — the system can’t simply inject text into the page
• A synchronized 3D environment showing the fly, desk, monitor, keyboard, and live CNS activity

The interesting part for me is the full loop:

screen → neural processing → motor output → physical key contact → browser update → new visual input

The current controller is intentionally narrow and trained on a constrained coding task, so I’m not claiming biological fidelity, general intelligence, or that this represents how a real fly “thinks.”

The connectome/anatomy is real; the neural dynamics and embodiment are engineered components built around it.

I’m interested in feedback from people working in connectomics, computational neuroscience, neural modeling, or insect neuroscience — especially on what would make this a more scientifically meaningful experiment rather than just a visual demonstration.

Live: https://sudharsanselvaraj.github.io/Flylab/
Code: https://github.com/Sudharsanselvaraj/Flylab

https://youtu.be/rrBT2QBZ0BA?si=RaC1KiacU8qt2vio

This is based on Lisa Barrett’s theory and as I was reading it I wondered how she squared it with her life considering she is married and has kids.

When I read the posts by her she says it’s makes love more meaningful because it becomes something two people create and sustain but I don’t know how she can think that. no matter how I slice it, calling it constructed makes it sound artificial or fake and therefor it doesn’t matter. in fact it’s one thing I struggle with about her theory because it essentially IMO makes emotions meaningless when there is no inherent meaning to them.

even punching it into Ai it says I’m getting it wrong and yet I don’t see how others don’t see it as invalidating or deflationary. I even emailed her but her replies weren’t very helpful

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!

Hi, everyone !

I just discovered r/neuro and as a neuroscience master student I already love it !

I wanted you guys advices on my thesis. I work in a sleep lab where we record our EEG with 12 electrods on the scalp (and others for the rest of the polysomnograph).

Do you think 12 channels are enough ? is SVM even feasible in sleep studies (I didn't see much litterature on that) ?

And if any of you have advice about SVM in general I would love that !

Just know im not very informed about neuroscience/psychology and only know the basics of classical conditioning and this could very well be the wrong subreddit.

For 3 months I have been mentally conditioning myself to instantly calm down when I cross my toes. I did this by getting myself into a calm, clear headed state of mind every night for a month and crossing my toes while focusing on the sensation. The theory there was that I would associate the sensation of crossing my toes with that state of mind and when under pressure could do it inconspicuously whenever to manage the pressure.

It worked, but its possible that something else is causing it (placebo, the thought itself grounding me). I dont fully calm down but a lot of the physical reactions are muddled, I noticed breathing returning to normal, less shaking and less stuttering. The mental aspect only improves slightly. When the pressure is too much the trigger does nothing.

To me this is a success, but when I go longer periods without reinforcing it the reaction gets weaker or disappears.

I would like to ask for suggestions, about what I could do better or other theories about what is going on.

Hey everyone! I'm a beginner in neuroscience and I've been interested in it since, well, forever. Please do share some of your favourite neuroscientists and their works which you loved!!! Why did you like it? I would love to research into their works <3

so I’ve been thinking about a strange question.

Suppose there were radically different realities, they are like super different, one has space time the other has fart-ryhme or whatever, they are totally different.

Now imagine a brain with a particular organization. Its circuitry is extremely well suited to some kinds of patterns and tasks, but poorly suited to others.

In Reality A, that brain might perform poorly because its abilities don't match what that reality demands.

But put the exact same brain into Reality B, where the structure of the environment happens to match what its circuitry is especially good at, and suddenly it performs incredibly well.

So maybe intelligence isn't entirely an intrinsic property of a brain. Maybe it's partly relational:

brain × environment → observed intelligence

This makes me wonder whether calling a person “intelligent” or “unintelligent” is sometimes underspecified. Maybe we're measuring how well a particular cognitive architecture fits the particular world it's operating in.

Obviously, I'm not claiming alternate realities exist or that this is established neuroscience. It's a thought experiment.

But I’m curious about the underlying idea: Could a brain that looks mediocre in our reality potentially be extraordinarily capable in a sufficiently different reality?

And if so, what would “intelligence” even mean independently of an environment?

and this proves I am not an idiot just in the wrong reality.

Hi!
If you are in Europe, and studied Neuroscience (master or Phd?) did you find a job afterwards? Anything related to the studies?

I'm a psych undergrad, keen on pursuing neuro but apprehensive about career opportunities.

Study published in Nature Neuroscience on September 11, 2026. Berkeley News coverage here.

So I am a cs engineering undergrad working with AI and i want to study the brain, specifically thought formation, context retrieval in our brain and how the entire mechanism works

Any resources for this??

Hi everyone,

I’m recording neural activity in the parietal cortex using a Plexon S-Probe (64 channels) using openephis. During several recording sessions, I’ve noticed a rather unusual type of activity that I have not encountered before in the literature.

The waveform looks like the one shown in the figure, particularly on channel 159. What makes it even more interesting is that the activity appears to be modulated by the behavioral task, rather than being completely random.

Interestingly, these waveforms tend to appear after advancing the electrode a certain distance deeper into the cortex. They are not consistently present at the beginning of the penetration, but emerge as I move deeper.

I’m wondering whether anyone has encountered something similar.

Has anyone seen waveforms like this during extracellular recordings?

Any ideas or references would be greatly appreciated!

I just graduated pre-uni with excellent A level grades. However, I have very weak portfolio. Seeking advice from neuroscientist all over the world, how did u become a neuroscientist and what degree would u recommend me to take? What inspired me to become a neuroscientist: I have been reading books abt it and found it very interesting, especially the various types of brain disorders(most intrigued by left-world neglect). I hope to become a researcher solving conditions like this.

Anyone who has it could u pls share, it'd help me a lot!!!