When it comes to black boxes, there is none more black than the human brain. Our grey matter is so complex, scientists lament, that it can’t quite understand itself.

But if we can’t grok our own brains, maybe the machines can do it for us. In the most recent issue of Nature Communications , researchers led by University of Pennsylvania psychologist Michael Kahana show that machine learning algorithms–notoriously inscrutable systems themselves–can be used to decipher and then enhance human recollection. How? By triggering the delivery of precisely day pulsations of energy to the brain.

Researchers, in other words, can use one black box to unlock the health risks of another. Which on one hand sounds like a instead elegant solution to an absurdly difficult problem, and on the other sounds like the beginning of a techno-pocalypse horror flick.

When it comes to brain measurings, the best records come from inside the cranium. But people–and institutional review boards–aren’t usually amenable to cracking open skulls in the name of science. So Kahana and his colleagues collaborated with 25 epilepsy patients, each of whom had between 100 and 200 electrodes implanted in their brain( to monitor seizure-related electrical activity ). Kahana and his squad piggybacked on those implants, using the electrodes to record high-resolution brain activity during memory tasks.

Machine discovering algorithms “ve learned to” associate patterns of electrode measurings with a patients’ likelihood of memorizing a word.

Kahana et al .

First, the researchers got a sense of what it looks like when a brain memorizes stuff. As individual patients read and attempted to internalize lists of words, Kahana and his squad met thousands of voltage measurings per second from each of the implanted electrodes. Afterward, they tested the patients’ recall–building up data covering which brain activity patterns was made in association with recollecting a word vs. forgetting it.

Then they did it again. And again. After two or three visits with each test subject, they &# x27 ;d compiled enough teach data to render patient-specific algorithm who are able to predict which terms each patient is very likely to remember–based on their electrode activity alone.

Here’s the kicker. These electrodes don’t just read neural activity; they can energize it, too. So the researchers tried nudging the brain to improve–or, as they put it, “rescue”–the formation of recollections in real time. Every few seconds, the subject would realize a new word, and the newly trained algorithm would decide whether the brain was ready to remember it. “A shut loop system lets us record the state of the subject &# x27; s brain, analyze it, and decide whether to trigger a stimulation, all in a few hundred milliseconds, ” Kahana says.

And it operated. The researchers &# x27; system improved patients &# x27; ability to recall words by an average of 15 percent.

This isn’t the first time Kahana’s lab has investigated the impacts of brain stimulant on recollection. Last year, the group proved that electrode heartbeats seemed to improve or worsen recollection, is dependent on when the researchers delivered them. In that survey, test subjects scored higher when the researchers induced memory-specific regions of the brain during sessions of low functionality( stimulant during high-functioning days had the opposite result ). It was a major finding, but therapeutically useless; the researchers could have been identify the link between recollection and brain states after the memory tests were performed. What you really crave, from a brain-enhancement standpoint, is to deliver pulses in the middle of memorization.

Now, Kahana and his colleagues appear to have closed the loop with the help of their machine learning algorithm. “Only instead of using it to identify images of cats, we &# x27; re employing it to build a decoder–something that they are able look at electrical activity and be seen whether the brain is in a state that &# x27; s conducive to learning, ” Kahana says. If the brain looks like it’s encoding remembrances effectively, the researchers leave it alone. If it isn &# x27; t, their system speedily delivers electrical pulses to jostle it into a higher-functioning state–like a pacemaker for the brain.

“It &# x27; s not a whomping consequence, but it &# x27; s definitely promising, ” says UC San Diego neuroscientist Bradley Voytek, who was unaffiliated with the study. The topic now is whether future work in this area will yield better outcomes. If patients &# x27; brains were implanted with more–and more precise–electrodes, algorithms could decipher more neural signatures, with more specificity, on smaller hour scales. More training data could help, too; most patients with epilepsy are only able to participate in analyses like this one for a few weeks at most, which restriction the time researchers can spend with them. A machine learning algorithm qualified on more than three conferences might perform better than the ones in Kahana &# x27; s latest study.

But even with higher resolving and more develop data, scientists will need to grapple with the implications of using opaque algorithms to study–and manipulate–brains. The fact remains that while Kahana’s system can improve word recall in specific circumstances, he doesn’t know exactly how it’s improving role. That’s the nature of machine learning.

Luckily, Kahana &# x27; s team has thought this through, and some algorithm are easier to scrutinize than others. For this particular analyse, the researchers utilized a simple linear classifier, which allowed them to describe some presumptions about how activity at individual electrodes might contribute to their simulate &# x27; s ability to discriminate between patterns of brain activity. “We can &# x27; t truly say at this phase if there are interactions between the features that we’re use to record brain activity, ” says UPenn psychologist Youssef Ezzyat, who supervised such studies &# x27; s machine learning analyses.

More complicated deep-learning techniques won &# x27; t inevitably translate to bigger cognitive improvements. But if they do, researchers could wind up is difficult to make sense of the machines’ decision to deliver brain-boosting electrical impulses. Or–if they become genuinely diabolical–withhold them.

Read more: https :// www.wired.com/ story/ ml-brain-boost /