A Huge New Data Set Pushes the Limits of Neuroscience

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A Huge New Data Set Pushes the Limits of Neuroscience
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The Allen Institute’s release includes recordings from a whopping 300,000 mouse neurons. Now the challenge is figuring out what to do with all that data.

So neuroscientists use an approach called “dimensionality reduction” to make such visualization possible—they take data from thousands of neurons and, by applying clever techniques from linear algebra, describe their activities using just a few variables. This is just what psychologists did in the 1990s to define their five major domains of human personality: openness, agreeableness, conscientiousness, extroversion, and neuroticism.

But the variables extracted from neural data can’t be expressed in a single word like “openness.” They are more like motifs, patterns of activity that span whole neural populations. A few of these motifs can define the axes of a plot, wherein every point represents a different combination of those motifs—its own unique activity profile.

There are downsides to reducing data from thousands of neurons down to just a few variables. Just like taking a 2D image of a 3D cityscape renders some buildings totally invisible, cramming a complex set of neuronal data down into only a few dimensions eliminates a great deal of detail. But working in a few dimensions is much more manageable than examining thousands of individual neurons at once.

But this vein of research is still in its early days, and scientists struggle to agree on what the patterns and trajectories mean. “People fight all the time about whether these things are factual,” says John Krakauer, professor of neurology and neuroscience at Johns Hopkins University. “Are they real? Can they be interpreted as easily [as single-neuron responses]? They don’t feel as grounded and concrete.

Bringing these trajectories down to earth will require developing new analytical tools, says Churchland—a task that will surely be facilitated by the availability of large-scale data sets like the Allen Institute’s. And the unique capacities of the institute, with its deep pockets and huge research staff, will enable it to produce greater masses of data to test those tools.

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