For decades, scientists have dreamed of building computer systems that could replicate the human brain's talent for learning new tasks. But it has been a daunting task.

To get an idea of the complexity, consider this:

• There are about 100 billion neurons in the brain.
• Each neuron forms synapses with many other neurons.
• A synapse is the gap between two neurons.
• With respect to a synapse, one neuron is known as the pre-synaptic neuron and the other is the postsynaptic neuron.
• The pre-synaptic neuron releases neurotransmitters, such as glutamate and GABA, which bind to receptors on the post-synaptic cell membrane, activating ion channels.
• Opening and closing those channels changes the cell's electrical potential.
• If the potential changes dramatically enough, the cell fires an electrical impulse called an action potential.
• All of this synaptic activity depends on the ion channels, which control the flow of charged atoms, such as sodium, potassium, and calcium.
• Those channels are also key to two processes known as long-term potentiation or LTP, and long-term depression or LTD, which strengthen and weaken synapses, respectively.

The MIT researchers have now taken a major step toward understanding the brain' learning process by designing a computer chip that mimics how the brain's neurons adapt in response to new information.

This phenomenon, known as "plasticity," is believed to underlie many brain functions, including learning and memory.

Using about 400 transistors, MIT's silicon chip can simulate the activity of a single brain synapse. And it is expected to help neuroscientists learn much more about how the brain works. It could also be used in neural prosthetic devices such as artificial retinas.

A paper describing the chip appeared recently in the Proceedings of the National Academy of Sciences.

The MIT researchers plan to use their chip to build systems to model specific neural functions, such as the visual processing system. Such systems could be much faster than digital computers.

Even on high-capacity computer systems, it takes hours or days to simulate a simple brain circuit. With the analog chip system, the simulation is even faster than the biological system itself.

Another potential application is building chips that can interface with biological systems. This could be useful in enabling communication between neural prosthetic devices, such as artificial retinas, and the brain. Further down the road, these chips could also become building blocks for artificial intelligence devices.