What does it mean to grow a mind?

The human mind is the north star for digital intelligence. But silicon can only do so much. Cortical is growing human neurons into silicon. Their reality is our simulation. We think these minds will learn better than any digital model and breathe life into our machines.

Human neural networks raised in a simulation

The neurons exist inside our Biological Intelligence Operating System (biOS). biOS runs the simulation and sends information about their environment, with positive or negative feedback. It interfaces with the neurons directly. As they react, their impulses affect their digital world.

Our first minds

The dishbrain is currently being developed at the CL0 laboratory in Melbourne, AU. We bring these neurons to life, and integrate them into The biOS with a mixture of hard silicon and soft tissue. Our first cohort have learnt to play Pong. They grow, adapt and learn as we do.

Silicon meets neuron

Neurons are cultivated inside a nutrient rich solution, supplying them everything they need to be happy and healthy. Their physical growth is across a silicon chip, which has a set of pins that send electrical impulses into the neural structure, and receive impulses back in return.

A direct connection to infinity

This creates the highest bandwidth connection possible between an organic neural network and a digital world. Our biOS composes their reality, sending information about it via electrical signals. It then converts the neuron's activity into actions inside that reality. Their world is mediated through our biOS.

The Ultimate Learning Machine

Those actions have a positive or negative effect in biOS, which the mind perceives, adapting to improve that feedback. The human neuron is self programming, infinitely flexible, the result of four billion years of evolution. What digital models try and emulate, we begin with.


There are many advantages to organic-digital intelligence. Lower power costs, more intuition, insight and creativity in our intelligences. But most importantly we are driven by three core questions.

What will we discover if our intelligences train themselves?

We know an organic mind is a better learner than any digital model. It can switch tasks easily, and bring learnings from one task to another. But more important is what we don’t know. What are the limits of a mind connected to infinity? What can it do with data it literally lives in?

What happens if we take a shortcut to generalised intelligence?

Machine Learning algorithms are a poor copy of the way an organic neural network functions. So we’re starting with the neuron, replacing decades of algorithms with millions of years of evolution. What happens as these native intelligences start solving the problems we’d previously left to software?

How can we surpass the limits of silicon?

Silicon is raw, rigid, unchanging. Our organic neural networks sit on top of this raw power, but the way they grow and evolve isn’t limited to the software they run on. There is no software, it's coded in their DNA. How will computing change as we shift from hard silicon to soft tissue?

RFN: Request For Neurons

The dishbrain is learning and growing in biOS today, and soon we’re opening an early access preview for selected developers. The biOS is our simulation environment, where you can program tasks, challenges and objectives for our minds. Join our developer program to get early access to our SDK, and secure training time with our minds.

Sign Up

What comes next

We’re not making smarter computers, more efficient data centers, or more personalised advertising. We’re doing this to see what happens. What happens if we grow a mind native to the infinite possibility space of digital computing?

We wonder what it will mean for digital spaces, for robotics, science, personal care. To explore the delineation between the personal mind, the distributed mind, digital and physical realities. To blur those boundaries. We wonder what it means to grow a mind, born of the physical world, but a native of the digital world, where that mind will go, and what it will teach us.

Wonder with us.