Ramin Hasani explains that Liquid AI's origins trace to studying the C. elegans worm, whose 300-neuron nervous system outperforms robotic control systems, motivating a biologically-grounded approach to neural network design. ✦ AI generated
Ramin Hasani · The Cognitive Revolution · 2026-07-04 · original ↗
starts at this moment · 9:16
“So maybe for starters, how would you tell the kind of broad story of Liquid AI leading up to what you're doing today and what the company's mission is today?”
this animal exhibits massive amount of kind of sensory reactive kind of behavior like amazing levels of control with 300 cells in its nervous system, you know, and that's was fascinating for us because this is much smaller than any neural network that performs control at that time like on on let's say like autonomous systems, you know, and it's wor can do better better, you know, dextrous movements and stuff
verbatim transcript · starts at 9:16
8:58brains you know and and we started looking into how one thing that I really liked I wanted to look at it from a first principle kind of approach like how neurons exchange information with each other you know we started looking into brain of worms you know small animals and and why worms this this specific worm see elegance we started working on the brain of the worm the
9:16reason behind it is that it was the only animal that in 2015 when I started this type of research as part of my PhD with my co-founder Matias Lechner, we um this was the only animal that we knew the entire nervous system as a whole. And this animal exhibits massive amount of kind of sensory reactive kind of behavior like amazing levels of control with 300 cells in its
9:41nervous system, you know, and that's was fascinating for us because this is much smaller than any neural network that performs control at that time like on on let's say like autonomous systems, you know, and it's wor can do better better, you know, dextrous movements and stuff like better than uh the best robotic systems that he actually had in in in the world you know so we thought that
10:02okay so let's start understanding how neurons exchange information in the brain of the worm from there on let's start building nervous system more complex than complex kind of neural circuits so that we can get to the stage where we can let's say like build the next animal build the next uh you know like like basically follow the path of evolution of nervous systems you know and see how how we can how we can rise
10:25and and evolve as as part of this thing so there are equations that describe the neuron neuronal dynamics of let's say like two neurons in the brain of C elegance because the worm is very small the neurons do not spike they are graded kind of neurons like they they they behave like in an electroonic way you know so they they're very similar to artificial neural networks that we have
10:48because they're also very differentiable right because you don't have spikes in the activity of neurons they're very differentiable that's why like it was even even nicer for us and more attractive for us because we could apply learning theory to these type of neural networks. Once constructed like let's say two neurons, four neurons, eight neurons, like let's say 100 neurons next to each other and then you start like
11:07training them. You can apply back propagation like as a differential kind of programming on top of a a a system that is built by by these type of uh um inspirations that we got from nature. The type of differential equations that were there were also very well behaved. you know, you can make them as complicated as you can, you know, and and and and then when they get more