A cat can recognize a face faster and more efficiently than a supercomputer.
That’s one reason a feline brain is the model for a biologically inspired computer project involving U-M.
Computer engineer Wei Lu has taken a step toward developing this revolutionary type of machine that could be capable of learning and recognizing, as well as making more complex decisions and performing more tasks simultaneously than conventional computers can.
Lu previously built a “memristor,” a device that replaces a traditional transistor and acts like a biological synapse, remembering past voltages it was subjected to. Now, he has demonstrated that this memristor can connect conventional circuits and support a process that is the basis for memory and learning in biological systems.
A paper on the research is published online in Nano Letters and is scheduled to appear in the forthcoming April edition of the journal.
“We are building a computer in the same way that nature builds a brain,” says Lu, an assistant professor in the Department of Electrical Engineering and Computer Science. “The idea is to use a completely different paradigm compared to conventional computers. The cat brain sets a realistic goal because it is much simpler than a human brain but still extremely difficult to replicate in complexity and efficiency.”
Today’s most sophisticated supercomputer can accomplish certain tasks with the brain functionality of a cat, but it’s a massive machine with more than 140,000 central processing units and a dedicated power supply. And it still performs 83 times slower than a cat’s brain, Lu wrote in his paper.
In a mammal’s brain, neurons are connected to each other by synapses, which act as reconfigurable switches that can form pathways linking thousands of neurons. Most importantly, synapses remember these pathways based on the strength and timing of electrical signals generated by the neurons.
In a conventional computer, logic and memory functions are located at different parts of the circuit and each computing unit is only connected to a handful of neighbors in the circuit. As a result, conventional computers execute code in a linear fashion, line by line, Lu says. They are excellent at performing relatively simple tasks with limited variables.
But a brain can perform many operations simultaneously, or in parallel. That’s how we can recognize a face in an instant, but even a supercomputer would take much, longer and consume much more energy in doing so.
So far, Lu has connected two electronic circuits with one memristor. He has demonstrated that this system is capable of a memory and learning process called “spike timing dependent plasticity.” This type of plasticity refers to the ability of connections between neurons to become stronger based on when they are stimulated in relation to each other. Spike timing dependent plasticity is thought to be the basis for memory and learning in mammalian brains.
The next step is to build a larger system, Lu says. His goal is achieve the sophistication of a supercomputer in a machine the size of a two-liter beverage container. That could be several years away.
The paper is titled “Nanoscale Memristor Device as Synapse in Neuromorphic System.”
