When it comes to things like image recognition, the human brain can completely outclass the most powerful computers on the market. It's able to dredge up images that are decades old—no bit-rot or obsolete formats in the wetware—in order to identify similarities or perform category recognition tasks. And, even though we have a decent idea of how individual neurons work, scientists are still struggling to understand how these cells manage to store and convey information. A study published over the weekend by Nature Neuroscience describes how researchers were able to track the maintenance of short-term, working memories in the neurons of a rat brain and, in the process, managed to read and write individual bits into a brain slice.

Working memory provides the short-term recall capacity that's essential for many basic tasks—it's where we put a phone number while we're remembering which pocket our cell phone is in, or hold the digits we're carrying while summing a series of numbers. For the most part, these memories are only held for a matter of seconds; we have to engage completely different processes to store them for the long term.

A brain structure called the hippocampus appears to be essential for the handling of both short- and long-term memories (as well as moving memories from short- to long-term storage). Previous work had indicated that there are populations of neurons in the hippocampus that, given a brief stimulus, would remain active for a matter of seconds; this activity was associated with working memory. Unfortunately, attempts to isolate these cells and observe the same sort of behavior under controlled conditions in culture haven't worked out.

A just-published paper relies on an experimental system that's somewhere in between the two: slices of the rat brain that contain the dentate gyrus, a structure within the hippocampus. The neurons can survive for a while when the slices are placed in culture, and the connections among most of them should be maintained. That means that researchers can explore brain activities that more closely approximate normal ones, but under better-controlled and more accessible conditions.

With the brain slices in place, the authors were quickly able to replicate the patterns observed in living animals, and identify the precise cells responsible. Using an electrode to activate a cell called a semilunar granule neuron would trigger a response in entire populations of neurons called hilar cells, which could still be detected over 10 seconds after the end of the stimulation (the half life of the activity occurred at about eight seconds). Those are the sorts of time scales typically associated with working memory.

A lot of the paper is spent identifying the precise combination of cells and receptors to get this extended burst of activity—glutamate signaling through NMDA receptors seems to be essential, as does calcium.

But the really interesting results, at least from the non-neurobiologist perspective, come from recordings of entire networks of the hilar cells. By triggering different combinations of input cells and seeing which cells responded with long-term activity, the authors were able to identify stable input/output combinations. In other words, they could identify (with an accuracy of about 90 percent) which stimulus was given based on the pattern of long-term activity it generated, even out to about 10 seconds after the input.

The authors don't make a big deal out of it in the paper, but this is the rough equivalent of writing a bit to the brain, and reading it back out 10 seconds later.

This isn't exactly a breakthrough in biological computing. As the student who performed much of the work, Phillip Larimer, put it, "It took us four years to be able to reproducibly store two bits of information for 10 seconds." Still, the cellular behavior he's helped identify matches up nicely with the actual experience of using working memory that we're all familiar with. And, by diving into the mechanisms in detail, the paper may provide some insight into the general question of how cells encode information.

Nature Neuroscience, 2009. DOI: 10.1038/nn.2458