Researchers are closing in on the rules that the brain uses to lay down memories. Discovery of this memory code could lead to the design of smarter computers and robots and even to new ways to peer into the human mind.
Anyone who has ever been in an earthquake has vivid memories of it: the ground shakes, trembles, buckles and heaves; the air fills with sounds of rumbling, cracking and shattering glass; cabinets fly open; books, dishes and knickknacks tumble from shelves. We remember such episodes–with striking clarity and for years afterward–because that is what our brains evolved to do: extract information from salient events and use that knowledge to guide our responses to similar situations in the future. This ability to learn from past experience allows all animals to adapt to a world that is complex and ever changing.
For decades, neuroscientists have attempted to unravel how the brain makes memories. Now, by combining a set of novel experiments with powerful mathematical analyses and an ability to record simultaneously the activity of more than 200 neurons in awake mice, my colleagues and I have discovered what we believe is the basic mechanism the brain uses to draw vital information from experiences and turn that information into memories. Our results add to a growing body of work indicating that a linear flow of signals from one neuron to another is not enough to explain how the brain represents perceptions and memories. Rather, the coordinated activity of large populations of neurons is needed.
Furthermore, our studies indicate that neuronal populations involved in encoding memories also extract the kind of generalized concepts that allow us to transform our daily experiences into knowledge and ideas. Our findings bring biologists closer to deciphering the universal neural code: the rules the brain follows to convert collections of electrical impulses into perception, memory, knowledge and, ultimately, behavior. Such understanding could allow investigators to develop more seamless brain-machine interfaces, design a whole new generation of smart computers and robots, and perhaps even assemble a codebook of the mind that would make it possible to decipher–by monitoring neural activity–what someone remembers and thinks.
My group’s research into the brain code grew out of work focused on the molecular basis of learning and memory. In the fall of 1999 we generated a strain of mice engineered to have improved memory. This “smart” mouse–nicknamed Doogie after the brainy young doctor in the early-1990s TV dramedy Doogie Howser, M.D.—learns faster and remembers things longer than wild-type mice. The work generated great interest and debate and even made the cover of Time magazine. But our findings left me asking, What exactly is a memory?
Scientists knew that converting perceptual experiences into long-lasting memories requires a brain region called the hippocampus. And we even knew what molecules are critical to the process, such as the NMDA receptor, which we altered to produce Doogie. But no one knew how, exactly, the activation of nerve cells in the brain represents memory. A few years ago I began to wonder if we could find a way to describe mathematically or physiologically what memory is. Could we identify the relevant neural network dynamic and visualize the activity pattern that occurs when a memory is formed?
For the better part of a century, neuroscientists had been attempting to discover which patterns of nerve cell activity represent information in the brain and how neural circuits process, modify and store information needed to control and shape behavior. Their earliest efforts involved simply trying to correlate neural activity–the frequency at which nerve cells fire–with some sort of measurable physiological or behavioral response. For example, in the mid-1920s Edgar Adrian performed electrical recordings on frog tissue and found that the firing rate of individual stretch nerves attached to a muscle varies with the amount of weight that is put on the muscle. This study was the first to suggest that information (in this case the intensity of a stimulus) can be conveyed by changes in neural activity–work for which he later won a Nobel Prize.
Since then, many researchers using a single electrode to monitor the activity of one neuron at a time have shown that, when stimulated, neurons in different areas of the brain also change their firing rates. For example, pioneering experiments by David H. Hubel and Torsten N. Wiesel demonstrated that the neurons in the primary visual cortex of cats, an area at the back of the brain, respond vigorously to the moving edges of a bar of light. Charles G. Gross of Princeton University and Robert Desimone of the Massachusetts Institute of Technology found that neurons in a different brain region of the monkey (the inferotemporal cortex) can alter their behavior in response to more complex stimuli, such as pictures of faces.