Synthetic brains are a long way from reality, but researchers at the University of Southern California, funded by the National Science Foundation, are taking the first steps to build neurons from carbon nanotubes that emulate human brain function.
"At this point we still don't know if building a synthetic brain is feasible," said Alice Parker, professor of electrical engineering. "It may take decades to realize anything close to the human brain but emulating pieces of the brain, such as a synthetic vision system or synthetic cochlea that interface successfully with a real brain may be available quite soon, and synthetic parts of the brain's cortex within decades."
The challenges to creating a synthetic brain are staggering. Unlike computer software that simulates brain function, a synthetic brain will include hardware that emulates brain cells, their amazingly complex connectivity and a concept Parker calls "plasticity," which allows the artificial neurons to learn through experience and adapt to changes in their environment the way real neurons do.
There is also the matter of scale. By 2022, with conventional technology, if the team could construct a synthetic brain that emulated real brain function, even crudely, it would take 100 billion artificial neurons and a very a large room to hold them.
"Obviously the technology will have to be downsized to aid a human being or be feasible as a robot brain," Parker said. Power is another consideration. The power requirements for a synthetic brain are staggering because a human brain never turns off. "In a transistor, things are on or off so it's a black-or-white situation, but in the brain there are also many shades of gray and power is continuously being consumed," Parker noted.
But before the researchers can tackle concerns of power and scale, they are building mathematical models that accurately reflect the Byzantine connections of all the neurons and demonstrate how the connections allow neurons to communicate with each other.
Each neuron in the cortex--a part of the brain that contributes significantly to conscious thought and intelligence--is connected to tens of thousands of other neurons. The researchers are also implementing the complex computations carried out by each neuron on all the inputs it receives from other neurons.
"It's a nonlinear phenomenon and almost impossible to model but that's what we're attempting to do," Parker said.
The researchers have shown that portions of a neuron can be modeled electronically using carbon nanotube circuit models and have performed detailed simulations of the circuit models. A single archetypical neuron, including excitatory and inhibitory synapses, has been modeled electronically and simulated. Parker and her co-researcher, Chongwu Zhou, are in the process of combining these circuit models of neurons to create a functional carbon nanotube circuit model of a small network of neurons. This small network of interconnected neurons will be simulated using the carbon nanotube models. This network demonstrates an interesting neural circuit that detects moving edges in a selected direction.
Parker believes carbon nanotubes are an ideal material to emulate brain function because their 3-D structure allows connectivity in all directions on all planes and because a carbon-based prosthesis is less likely to be rejected by the human body than one made from inorganic materials. But their invasive nature could result in them invading surrounding tissue and prompting lesions and cancers.
"It's a possibility and something else that needs to be addressed for the technology to be feasible," Parker said.
As the researchers move ahead with their mathematical modeling and neuron construction, beginning with a single synapse, they ponder "plasticity," neuroscientists' term for the brain's ability to learn and adapt to change. "Our brains can grow new neurons and the synapses between them in an hour--a remarkable biological feature that is difficult to emulate from an engineering perspective," Parker said.
Emulating such plasticity in a synthetic brain will require a major leap in technology, similar to the leap from cathode ray tubes to transistors. "We don't know what the new technology will look like yet, but it will be a technology that can self-assemble and reshape itself. As we work in the lab building neurons or constructing mathematical models, we must consider the requirement of plasticity, even if we don't yet know what it looks like."
Aside from the daunting technological challenges, a synthetic brain or brain components will also raise ethical and environmental issues. The role of emotions in learning are just beginning to be understood, and it appears they are incredibly important to brain function.
"Based on what I know right now, emotions would have to be included for a synthetic brain to be able to learn," Parker said. "It's important to understand their cause and effect."