Two thousand years ago the ancient Greeks looked into the night sky and saw the geometric forms that occur among the stars: a hunter, a lion, a bowl of water. In a sense, they used these constellations to give a sense of the randomly scattered stars in the Universe. Transforming astronomy in forms, they have found a way to organize and give meaning to highly complex system. Of course, the Greeks got it wrong: most stars in a constellation generally irrelevant to each other do not have. But their approach has continued to live.
This week, the Blue Brain Project (project "Blue brain") has proposed an amazing idea that can explain the complexity of the human brain. Using algebraic topology — a type of mathematics that "designs" the complex compounds in the form of graphs scientists mapped the way complex functions which arise from the structure of neural networks.
And here's the thing: even though our brain physically takes place in our three-dimensional world, its internal relations — mathematically speaking — are functioning on a much higher dimensional space. Humanly speaking, the Assembly and disassembly of neural connections in the highest degree complex, even more than expected. But now we have the language that describes them.
"We found a world that I never expected to see," says Dr. Henry Markram, Director Blue Brain Project and Professor at EPFL in Lausanne, Switzerland, leading the study.
Perhaps that is why the brain was so difficult to understand, he says. "Mathematics, usually applied to research networks cannot detect vysokonapornye of structure and space, which we now see distinctly."the
When we think about the brain, come up with branching neurons and soft tissue — it is a three-dimensional object. In the language of physics, no miniature mini brain hidden in our own, no, our neurons do not move to some higher level of existence when activated.
Outside of physics "dimension" — it was just a funny way of describing complexity. Take a group of three neurons that work together (A, B and C), for example. Now think about how many ways to connect them. Since the information is usually transmitted only one way from neuron to its partner, And can only be associated with B or C. Topologically speaking, the dimension is equal to two.
Similarly, a group of four neurons has a dimension of three, five — four. The more neurons in the group, the higher the dimension, so the system has become increasingly complex.
"our study does not describe the dimension of the spatial dimension, rather the topological dimension geometric objects that we described. 7 - or 11-dimensional simplex will be included in the physical three-dimensional space," explains the study's author, Max Nolte, graduate student, EPFL.the
To begin to dismantle the organization of the brain, scientists started with functional blocks called simplices. Each simplex represents a particular group of neurons connected to each other in a very specific order.
One neuron is very important and the first one listens to all the neurons, and the few others are listening and talking with those who do not listen, says Nolte. "This special structure ensures that the neurons will listen to really understand the talking neurons in the brain, where it is always millions of neurons all talking at once, as the crowd in the stadium".
As before, the dimension describes the complexity of the simplex.
In six different virtual brain, each of which was restored from the experimental data obtained in rats, the scientists looked for signs of these abstract mathematical objects. Incredibly, virtual brains contain extremely complex the simplices up to the seventh dimension — and about 80 million "groups" of smaller dimension neurons.
A Huge number of simplices that are hidden inside the brain, suggests that each neuron is part of a huge number of functional groups is much greater than previously thought, says Nolte.the
If the simplices — the building blocks, how are they going to education is even more complex networks?
When the team was subjected to its virtual brain stimulation, neurons gathered in a complex network, like LEGO bricks formed the castle. But this relationship, again, not necessarily physical. The neurons are connected as in a social graph, and these graphs form a network or another vysokonapornye structure.
The Fit wasn't perfect: between vysokonapornye structures were "holes", places in which there was no context for the formation of a new network.
As with simplices, have holes too, their dimension. In some ways, says Nolte, "dimension hole describes how close was the simplices to reach a higher dimension", or how the building blocks relate to each other.
The Emergence of higher dimensional holes tells us that the neurons in the network respond to stimuli (incentives) "very organized manner," says Dr. Ran Levi the University of Aberdeen, who also worked on this article.
When we look at the brain's response to a stimulus over time, we see that the geometrical objects are formed and then collapse when building a functional network, says levy.
First, the brain receives more than the simple neural networks to construct one-dimensional "frame". Then these networks are connected in a two-dimensional "wall" with "holes" between them. Subsequent and more vysokonapornye structure and holes are formed until, until they reach a peak organization which connections between neurons nor required.
Then the whole structure collapses, freeing the simplices for the next task, like a sand castle materializes and then decays away.
"We don't know what makes the brain, forming these cavities," says levy. But what is known for sure is that the neurons must be activated "fantastically orderly manner" to this arrogant structure appeared.
"it is clear that this hyperargininemia activity is not just a coincidence. This can be the key to understanding what happens when the brain is active," says levy.the
Scientists also have discovered how neurons in the same or different groups communicate with each other after the stimulus. It all depends on whether they are in vysokorazvityh structures and groups. Imagine two "strangers" of the neuron that communicate, says Nolte. They probably say a lot of unrelated things, because they don't know each other.
Now imagine that after the stimulus, they form vysokonapornye network. Like Twitter, this network allows a single neuron to hear the other, and they can even mimic others. If they're both "followit" dozens of other people, their tweets can be even more similar, because thoughts depends on the crowd.
"Using simplices, we not only count how many total people they followed, but these people are linked," says Nolte. The more interconnected two neurons, the more simplices to which they belong — the more it looks like they are activated in response to a stimulus.
This obviously shows the importance of the functional structure of the brain: the structure determines the emergence of correlated activity, says levy.
Previous studies have shown that the physical structure of neurons and synapses affects the pattern of activity; we now know that it is also important to their relationship in "vysokodispersnom space."
In the future, the team hopes to understand how these complex abstract networks define our thinking and behavior.
"It's like a searchable dictionary which translates the incomprehensible language into another language which is familiar to us, even if we do not fully understand texts written in this language," says levy.
It's time to decipher these stories, adds the scientist....
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