[frisco]

Interestingly, this is not a new result; people have been doing stuff like this since at least the 90s, most notably Steve Potter at GA Tech and Tom DeMarse in Florida.[1][2] (I built a shitty counterstrike aimbot using a cultured neural network in college based on their papers.)

There was a lot of coverage back in 2004 when DeMarse hooked it up to a flight simulator and claimed it was flying an F-22 [3] (lol, but I don't blame him too much...)

The basic idea is that if you culture neurons on an electrode array (not that hard) you can pick some electrodes to be "inputs" and some to be "outputs" and then when you stimulate both ends the cells wire together more or less according to Hebb's rule[4] and can learn fairly complex nonlinear mappings.

On the other hand, these cultures have essentially no advantage over digital computers and modern machine learning models. Once you get through the initial cool factor, you realize it's a pain to keep the culture perfectly sterile, fed, supplied with the right gases, among many other practical problems, for a model which is just much less powerful, introspectable, and debuggable than is possible on digital computers.

[1] https://bpb-us-w2.wpmucdn.com/sites.gatech.edu/dist/f/516/fi...

[2] https://potterlab.gatech.edu/labs/potter/animat/

[3] https://www.cnn.com/2004/TECH/11/02/brain.dish/

[4] https://en.wikipedia.org/wiki/Hebbian_theory

[SubiculumCode]

Once AI gets over the initial cool factor that humans are wet-tech, they'll realize it's a pain to keep the human culture perfectly sterile, fed, supplied with the right gases, among many other practical problems, only for a model which is just much less powerful, introspectable, and debuggable than is possible on digital computers.

[ungamedplayer]

That was beautiful.

[fnordpiglet]

I was with you up to “once you get over the cool factor.” It seems impossible to get over how cool it is to have a minibrain playing video games. Having one of those at home must really impress the girls.

[UniverseHacker]

Moreover, if there are girls not impressed by this, you will know, and have really dodged a bullet.

[seydor]

"played video games" is overstatement. There was a slight increase in the performance with the particular setup that they used. It was not as straightforward as it sounds. This kind of science is still in its infancy

[spaceman_2020]

“Meet my brother. He’s adopted”

[auastro]

One of the early CorticalLabs founders here. This is like dissing AlphaZero because "This is not a new result; computers have been playing chess since the 50s!". We are standing, as always, on the shoulders of giants. Steve Potter is one of our advisors.

We've improved on every axis 10x. We process over 1000 signal channels in real-time and respond with sub-millisecond latency from our simulated environment. We've recorded thousands of hours of play time from mouse and human neurons. We're investigating biological learning with top neuroscientists from around the globe. This is by far the most rigorous, extensive and technologically advanced work on in-vitro learning ever produced.

Our work goes well beyond Hebbian, "fire together, wire together", We have follow up papers in the pipeline that study internal non-linear dynamics and show how whole-network dynamics changes during game play and learning. Being able to observe and measure cognition has huge applications to drug testing and discovery.

For background, frisco (the above commenter) helped start NeuralLink. Consider this, our DishBrain is a completely reproducible, highly controlled test bed for brain computer interfaces. This will massively accelerate neural interface development, all without sacrificing any chimpanzees.

> On the other hand, these cultures have essentially no advantage over digital computers and modern machine learning models

The brain is the single existing example of general intelligence. A human brain can do more computation than our largest super computers with 20W of power (a million times more efficient). Trillions of interacting synaptic circuits, rewiring themselves on the molecular level. Biological learning is the only game in town, honed by a eons of evolution. There are fundamental physical limits to hot slabs of silicon. Do you have a single credible proposal for building such a machine that isn't growing one?

> (I built a shitty counterstrike aimbot using a cultured neural network in college based on their papers.)

Nice humble brag. I trained neural networks from my bedroom in highschool in 2002. There is a long road between a cool university project and building a world class neuroscience R&D company, you know that!

CoriticalLabs is always open to collaborations. We're here to talk when you want to integrate some of our cutting-edge neuroscience technology with your work. Instead grumbling about the 90's, let's look forward to what neuroscience looks like in the 2030's

[alphaursaemin]

> The brain is the single existing example of general intelligence.

This is incorrect. It is not pedantic to point out that we have never interacted with a "brain" in isolation: the human brain is an organ of the human organism. The human being is the single existing example of general intelligence.

> let's look forward to what neuroscience looks like in the 2030's

This is very interesting science without question. Are there existing ethical and moral frameworks guiding the development of your field?

[frisco]

All I’m saying is that I think it will be challenging to produce a commercial product that achieves product-market fit for an application other than basic neuroscience research. It’s a cool tool but the practical drawbacks are myriad, and when you say “the brain is the single existing example of general intelligence,” that’s true of the whole thing, with glial ion buffering, ephaptic coupling, global oscillations, and so much more. We should be honest here: the system being studied in DishBrain is very far removed from that, so it’s tough to use the existence proof like you are doing.

I hope I don’t come across as uncivil, but you guys alienated a lot of people both in how you talked about “sentience” and also seemed to heavily hype this as totally novel.

I would never root against cool progress in neural engineering, but I would be curious as to what you think your first big product will be based on this. Past attempts have usually ended up pivoting to stuff like artificial noses.

Edit: I tried to ignore it but the bad faith attack on neuralink, which, look, I have complicated feelings about too — you should know the animal use data in the press is extremely out of context (to the point of simply being wrong) and also neuralink has had zero chimpanzees in its entire history.

[thaumasiotes]

> A human brain can do more computation than our largest super computers with 20W of power

The power needs of the human brain are likely to be measured quite accurately.

The same is not true of the "amount of computation" performed by the brain. How are you measuring that?

[xvilka]

> On the other hand, these cultures have essentially no advantage over digital computers and modern machine learning models.

Absolutely false. While it's indeed hard to keep it alive, real neurons are far more sophisticated than what AI researchers think they are. Modern digital so called neural networks are built on the outdated and oversimplified knowledge of neuron model, almost a century-old by now.

[p1esk]

Modern CMOS transistors are extremely sophisticated devices (you need a very complicated model with hundreds of parameters to simulate all kinds of quantum effects to predict its behavior). Yet all it does is one simple function - it's an on/off switch.

[seydor]

sophisticated is not a scientific word. they are complex and complicated, and the voltage dynamics across their elaborate membrane takes a lot of computers to simulate. But we don't really know what it is doing or if it is particularly sophisticated. Nature has found a lot of complex solutions to simple problems because it does not know better. We don't know how well it did with intelligence

[UniverseHacker]

> The basic idea is that if you culture neurons on an electrode array (not that hard) you can pick some electrodes to be "inputs" and some to be "outputs" and then when you stimulate both ends the cells wire together more or less according to Hebb's rule[4] and can learn fairly complex nonlinear mappings.

This is fascinating, can you clarify it a bit? Do you 'stimulate', e.g. apply electrical potential to both the inputs and outputs to represent each instance of training data, without any physical distinction between input and output at that stage? And then if you apply the potential only the inputs, you can then read predictions on the outputs?

[api]

What I always wonder about with these systems is how feedback was delivered to the cultured neurons. How do we tell them they're doing things correctly? Or is this some form of unsupervised learning with them?

[seydor]

the original paper is available https://www.cell.com/neuron/fulltext/S0896-6273(22)00806-6

They used a specific region of the electrode array to deliver the "reward" signal which was a regular predictable pulse pattern . An error was represented with unpredictable activity

[spagettnet]

Do you have a writeup or video of the aim bot you made? Would love to see it!

[sroussey]

We have lots of these cultures around for drug testing. I wonder if the “brain” playing pong affects the tests in any way.

[ge96]

Tangent: was thinking it would be cool if you had a bio mass that could connect to a pcie slot and act as a graphics card. That would be some really impressive tech. Build circuits in the goo with floating particles.