
The first computer with human neurons has been created
It's called CL1 and it was made by Australian start-up Cortical Labs
April 14th, 2025
In the landscape of emerging technologies, one of the most fascinating and talked-about projects comes from Australia and is called CL1. It is being developed by Cortical Labs, a biotech startup from the country that has created what is being called the world's first biological computer. Unlike traditional computing systems that simulate biological neural networks, CL1 is made using real human nerve cells, grown in a lab and integrated into a chip. The goal of the project is to offer a new tool to study neurological diseases, test drugs under controlled conditions, and contribute to understanding the fundamental mechanisms of the human brain. «We're on a mission to transform the world through human computing,» reads the Australian startup’s website. And while at first glance this statement might seem overly ambitious, it actually reflects a paradigm shift: directly using biological elements to enhance computing, rather than imitating the functioning of the human brain through mathematical models.
The functioning of CL1 is made possible thanks to advanced cell engineering technology. It all starts with blood samples donated voluntarily. From these, researchers isolate certain cells that are then genetically reprogrammed to return to a state similar to embryonic stem cells. These “reprogrammed” cells are called “induced pluripotent stem cells,” or iPSCs. But let’s take a step back: what are embryonic stem cells? They are cells present in the early stages of embryo development. They have an extraordinary characteristic: they are called “pluripotent,” meaning they can give rise to any type of cell in the human body – such as neurons, heart cells, skin cells, liver cells, and so on. iPSCs are therefore a lab-generated version of these “primitive” cells, created without the need to use embryos: starting from adult cells (like blood cells), they are brought back to a primordial, embryo-like state through the introduction of specific genetic factors. In the case of CL1, the iPSCs are directed to become neurons, that is, nerve cells capable of receiving and transmitting signals. Once matured, these neurons are placed on a specially designed chip. Here, the neurons begin to form connections with each other – synapses – and communicate in a way very similar to the human brain. In other words, a real, living biological neural network develops, capable of processing information and responding to external stimuli.
But what are the real applications of this technology? According to researchers, CL1 could represent a new frontier in the study of neurodegenerative diseases such as Alzheimer’s, Parkinson’s, or cognitive disorders. With a functioning human neural system available, it is possible to observe drug reactions in real time, explore cellular dynamics, and better understand the degenerative mechanisms that lead to the loss of specific brain functions. Furthermore, compared to traditional artificial intelligence models, which require large amounts of data and computing power to learn, biological neural networks show surprising efficiency: they can adapt to new stimuli and update much faster, with fewer inputs. Despite its potential, the CL1 project is still in the experimental phase and has some limitations. One of the main constraints is the lifespan of the neurons, which, even under optimal conditions, live for about six months on average, as explained by Cortical Labs scientists. After this period, the cells must be replaced, restarting the cultivation and integration process from scratch. The idea from Cortical Labs raises a further question: why continue to develop ever more complex AI models to imitate the human brain, when in the future it might be possible to directly use real neurons? Scientific research is still far from achieving “living computers” capable of even basic cognitive functions, but in the long term, the evolution of these technologies could lead to devices capable of learning, adapting, and collaborating with conventional artificial intelligence.