In vitro biological system of cultured brain cells has learned to play Pong
nature.com
nature.com
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/
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
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?
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.
Our vision is incredibly ambitious. We can't build a whole brain yet, only small 2D fragments. We have a roadmap that goes all the way to a complete synthetic biological intelligence. The short and medium term milestones are concrete, achievable and valuable. The long term goals are more speculative, we're clear about that. It's a path, a tightrope, but still a path.
> [...] 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.
We clearly defined our terms, our paper was accepted via a long peer review process into a prestigious academic journal. We coauthored with multiple top neuroscientists from around the world. Our discussion section alone has more citations than most entire papers. If scientists are "alienated" by this, it's a grievance that we cannot remedy.
Our work was hyped, we hyped it, it deserves to be hyped. Can you cite an example in our own words where we claim our work is totally novel?
> 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.
Please accept my apologies; it was meant to be more collaborative. I really do think that our system could be used to reduce the need for animal sacrifices and this is a good thing. I also believe you take making animal sacrifices seriously.
- in this field, monkeys are high value animals and experimenters will often work with the same ones for many years; they are not, generally speaking, a high throughput model.
- to the extent a company does need to go through a large number of animals for a study, the way this works is you start by figuring out all of the problems you might be worried about, and choosing some rarity threshold to verify absence of (safety against), and then animal numbers are derived from the power calculation. For example, to rule out a potential complication to no more than 1% of patients with 95% confidence… you need a lot of animals, especially considering multiple study arms. This is the values tradeoff we as a society have chosen to make and empower our regulators to enforce. There is often a negotiation for the least controversial species to use that will satisfy the scientific goals.
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?
I'm not sure this makes sense. Here is a simple dynamic programming problem from Project Euler: https://projecteuler.net/problem=67
You can estimate the amount of information being processed in a few different ways. But that's not really relevant; the whole point of solving this problem is that you can do the same job with less computation than it looks like you need.
There is no particular connection between "amount of information processed" and "amount of computation performed".
If the brain's job could be hypothetically done by some optimized system using an picowatt is irrelevant. We don't have such a system.
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.
Sorry but that's not how evolution works at all. You are essentially postulating that evolution results in efficient outcomes given enough time whereas there are many, many examples of evolution delivering results but clearly sub-optimal ones. It's not a given that evolution will lead to an efficient solution, it's not even a given that it will lead to a solution at all.
This is an example. Epigenetically driven evolution in arabodopsis, which protects certain regions from mutation. In a very limited sense, evolution might be said to "care" about something here, as it is kind of taking direction from the environment, not simply acting on uniformly random mutations. Nothing like this is known in animals or most anywhere else afaik.
You'd be hard-pressed to find an expert who believes any of the current crop of LLMs have a similar capacity to human brains.
I urge you to study more seriously about the things you seem so eager to speculate about before publishing underinformed opinions in public spaces.
Is that what you mean when you say "capacity"? Does not seem very relevant in the context of our discussion. If it's not what you have in mind, I'd appreciate a link to the Wikipedia article on "ML model capacity" or whatever specific term experts use to represent the concept.
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?
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
https://www.cell.com/cms/10.1016/j.neuron.2022.09.001/attach...
It's not "playing well". The experiment is set up so as to reinforce (through LTP) its dynamics when it performs correct rather than wrong. This ends up "wobbling the paddle around" with a statistically significant better performance than untrained . This is not a brain that knows what it's doing, but a few thousand neurons apparently able to undergo plasticity
Does that come from somewhere in the paper? I'm seeing "10^6 cells plated onto each MEA" and trying to determine if that's the size of each "DishBrain" or if the number is pared down before actual tests are carried out.
Growing Human Neurons Connected to a Computer - https://www.youtube.com/watch?v=V2YDApNRK3g
Connecting Neurons to a Computer: The New Plan - https://www.youtube.com/watch?v=p1C0qpqpAWc
A goal of the paper is to produce a system that can be used by researchers to experiment on neurons from different sources. It mentions its use of a few different cell lines, from mice and humans both, so at least some DishBrain runs used human cells. The human cells were from cell line ATCC PCS-201-010, which are stem cells derived from the dermal cells of a newborn baby's foreskin. The authors coaxed those stem cells to differentiate into neurons.
> how many do you have to grow before it becomes an ethics issue
The paper says they used methods to "achieve a dorsal forebrain patterning" and plated 10^6 cells on each electrode array used for testing (about 5x the brain cells of a mosquito, or a tenth of the cells in a mouse brain). Note that I have no idea if all million of those cells survived until the experiments were carried out, or if they were all actually used in each "brain" (maybe they were segmented out?)
I think the ethical issues are going to depend largely on how much the structure of the neurons affects awareness. A pile of 10 million random mouse neurons on a plate probably doesn't have anywhere near the same value of experience that a 10 million neuron actual mouse brain has. But I'd say if you're okay with experimenting on mice, 1 million neurons in a petri dish is very ethically safe in comparison. Agreed that we're starting to get to the point where it's more questionable though.
In particular, I question the significance of neural activity in the absence of sensory organs.
One might argue that at some critical mass / neuron count, presumably well above mouse-level, there is the potential of sentience. OK, but what is sentience in the absence of sensation?
I don't know the answer to that question! But I'm fairly confident I don't place its well-being high on the ethical totem pole.
Please just wire up some GPS and LIDAR to humans and make us better drivers.
(It's OK, I'm an expert at Pong.)
When it comes to brains, canonically Hannibal is fond of eating them straight out of the skull, with the owner still alive and conscious.
I've tried cow brain twice and it was delicious.
I really hope there are tiny turtles learning martial arts right this time
We don’t know, of course. Perhaps consciousness isn’t possible without a live body. Or perhaps it takes just a few hundred thousand neurons.
In the meantime, you run the risk of torturing a potentially conscious entity for…what gain exactly?
In earlier times, the ownership came out differently
GPU supercomputers no longer necessary. Just a BrainStation or two.