‘Brain’ In A Dish Acts As Autopilot, Living Computer

University of Florida researcher Thomas DeMarse holds a glass dish on October 12, 2004, containing a “brain” — a living network of 25,000 rat brain cells connected to an array of 60 electrodes that can interact with a computer to fly a simulated F-22 fighter plane. During this interaction, scientists can observe how the neurons communicate as a network, and send and respond to signals from the computer and each other, in an effort to understand and model the computational power of the brain.
(University of Florida/Ray Carson)

GAINESVILLE, Fla. — A University of Florida scientist has grown a living “brain” that can fly a simulated plane, giving scientists a novel way to observe how brain cells function as a network.

The “brain” — a collection of 25,000 living neurons, or nerve cells, taken from a rat’s brain and cultured inside a glass dish — gives scientists a unique real-time window into the brain at the cellular level. By watching the brain cells interact, scientists hope to understand what causes neural disorders such as epilepsy and to determine noninvasive ways to intervene. As living computers, they may someday be used to fly small unmanned airplanes or handle tasks that are dangerous for humans, such as search-and-rescue missions or bomb damage assessments.

“We’re interested in studying how brains compute,” said Thomas DeMarse, the UF professor of biomedical engineering who designed the study. “If you think about your brain, and learning and the memory process, I can ask you questions about when you were 5 years old and you can retrieve information. That’s a tremendous capacity for memory. In fact, you perform fairly simple tasks that you would think a computer would easily be able to accomplish, but in fact it can’t.”

While computers are very fast at processing some kinds of information, they can’t approach the flexibility of the human brain, DeMarse said. In particular, brains can easily make certain kinds of computations – such as recognizing an unfamiliar piece of furniture as a table or a lamp – that are very difficult to program into today’s computers.

“If we can extract the rules of how these neural networks are doing computations like pattern recognition, we can apply that to create novel computing systems,” he said.

DeMarse experimental “brain” interacts with an F-22 fighter jet flight simulator through a specially designed plate called a multi-electrode array and a common desktop computer.

“It’s essentially a dish with 60 electrodes arranged in a grid at the bottom,” DeMarse said. “Over that we put the living cortical neurons from rats, which rapidly begin to reconnect themselves, forming a living neural network – a brain.”

The brain and the simulator establish a two-way connection, similar to how neurons receive and interpret signals from each other to control our bodies. By observing how the nerve cells interact with the simulator, scientists can decode how a neural network establishes connections and begins to compute, DeMarse said.

When DeMarse first puts the neurons in the dish, they look like little more than grains of sand sprinkled in water. However, individual neurons soon begin to extend microscopic lines toward each other, making connections that represent neural processes. “You see one extend a process, pull it back, extend it out – and it may do that a couple of times, just sampling who’s next to it, until over time the connectivity starts to establish itself,” he said. “(The brain is) getting its network to the point where it’s a live computation device.”

To control the simulated aircraft, the neurons first receive information from the computer about flight conditions: whether the plane is flying straight and level or is tilted to the left or to the right. The neurons then analyze the data and respond by sending signals to the plane’s controls. Those signals alter the flight path and new information is sent to the neurons, creating a feedback system.

“Initially when we hook up this brain to a flight simulator, it doesn’t know how to control the aircraft,” DeMarse said. “So you hook it up and the aircraft simply drifts randomly. And as the data comes in, it slowly modifies the (neural) network so over time, the network gradually learns to fly the aircraft.”

Although the brain currently is able to control the pitch and roll of the simulated aircraft in weather conditions ranging from blue skies to stormy, hurricane-force winds, the underlying goal is a more fundamental understanding of how neurons interact as a network, DeMarse said.

“There’s a lot of data out there that will tell you that the computation that’s going on here isn’t based on just one neuron. The computational property is actually an emergent property of hundreds or thousands of neurons cooperating to produce the amazing processing power of the brain.”

With Jose Principe, a UF distinguished professor of electrical engineering and director of UF’s Computational NeuroEngineering Laboratory, DeMarse has a $500,000 National Science Foundation grant to create a mathematical model that reproduces how the neurons compute.

These living neural networks are being used to pursue a variety of engineering and neurobiology research goals, said Steven Potter, an assistant professor in the Georgia Tech/Emory Department of Biomedical Engineering who uses cultured brain cells to study learning and memory. DeMarse was a postdoctoral researcher in Potter’s laboratory at Georgia Tech before he arrived at UF.

“A lot of people have been interested in what changes in the brains of animals and people when they are learning things,” Potter said. “We’re interested in getting down into the network and cellular mechanisms, which is hard to do in living animals. And the engineering goal would be to get ideas from this system about how brains compute and process information.”

Though the “brain” can successfully control a flight simulation program, more elaborate applications are a long way off, DeMarse said. “We’re just starting out. But using this model will help us understand the crucial bit of information between inputs and the stuff that comes out,” he said. “And you can imagine the more you learn about that, the more you can harness the computation of these neurons into a wide range of applications.”