[sciborg2]

How Brain Scientists Forgot That Brains Have Owners

John Krakaeur, a neuroscientist at Johns Hopkins Hospital, has been asked to BRAIN Initiative meetings before, and describes it like “Maleficent being invited to Sleeping Beauty’s birthday.” That’s because he and four like-minded friends have become increasingly disenchanted by their colleagues’ obsession with their toys. And in a 2017 paper that’s part philosophical treatise and part shot across the bow, they argue that this technological fetish is leading the field astray. “People think technology + big data + machine learning = science,” says Krakauer. “And it’s not.”

He and his fellow curmudgeons argue that brains are special because of the behavior they create—everything from a predator’s pounce to a baby’s cry. But the study of such behavior is being de-prioritized, or studied “almost as an afterthought.” Instead, neuroscientists have been focusing on using their new tools to study individual neurons, or networks of neurons. According to Krakauer, the unspoken assumption is that if we collect enough data about the parts, the workings of the whole will become clear. If we fully understand the molecules that dance across a synapse, or the electrical pulses that zoom along a neuron, or the web of connections formed by many neurons, we will eventually solve the mysteries of learning, memory, emotion, and more. “The fallacy is that more of the same kind of work in the infinitely postponed future will transform into knowing why that mother’s crying or why I’m feeling this way,” says Krakauer. And, as he and his colleagues argue, it will not.

That’s because behavior is an emergent property—it arises from large groups of neurons working together, and isn’t apparent from studying any single one. You can draw parallels with the flocking of birds. Biologists have long wondered how they manage to wheel about the skies in perfect coordination, as if they were a single entity. In the 1980s, computer scientists showed that this can happen if each bird obeys a few simple rules, which dictate their distance and alignment relative to their peers. From these simple individual rules, collective complexity emerges.

But you would never have been able to predict the latter from the former....

[TaoHorror]

But the why of it emerging has no tether to it's component parts is not certain and still likely not a waste of time to study even if true such research gets us no further in that specific direction. The stack yields something that is a mystery of how it can do so - as long as it's a mystery, studying in all directions seems warranted. They may be right in that the community has blind spots, but to outright call it as "the wrong direction", doesn't appear their argumentation is convincing, but I concede this is just an annotate of a likely bigger work/thought, so maybe their entire treatise has legs.