Blue Brain Project accurately predicts connections between neurons

September 17, 2012
Copyright: EPFL / Blue Brain Project

One of the greatest challenges in neuroscience is to identify the map of synaptic connections between neurons. Called the "connectome," it is the holy grail that will explain how information flows in the brain. In a landmark paper, published the week of 17th of September in PNAS, the EPFL's Blue Brain Project (BBP) has identified key principles that determine synapse-scale connectivity by virtually reconstructing a cortical microcircuit and comparing it to a mammalian sample. These principles now make it possible to predict the locations of synapses in the neocortex.

"This is a major breakthrough, because it would otherwise take decades, if not centuries, to map the location of each synapse in the brain and it also makes it so much easier now to build accurate models," says Henry Markram, head of the BBP.

A longstanding neuroscientific mystery has been whether all the grow independently and just take what they get as their branches bump into each other, or are the branches of each neuron specifically guided by to find all its target. To solve the mystery, researchers looked in a virtual reconstruction of a cortical to see where the branches bumped into each other. To their great surprise, they found that the locations on the model matched that of found in the equivalent real-brain circuit with an accuracy ranging from 75 percent to 95 percent.

This means that neurons grow as independently of each other as physically possible and mostly form synapses at the locations where they randomly bump into each other. A few exceptions were also discovered pointing out special cases where signals are used by neurons to change the statistical connectivity. By taking these exceptions into account, the Blue Brain team can now make a near perfect prediction of the locations of all the synapses formed inside the circuit.

Copyright: EPFL / Blue Brain Project

Virtual Reconstruction

The goal of the BBP is to integrate knowledge from all the specialised branches of , to derive from it the fundamental principles that govern brain structure and function, and ultimately, to reconstruct the brains of different species – including the human brain – in silico. The current paper provides yet another proof-of-concept for the approach, by demonstrating for the first time that the distribution of synapses or neuronal connections in the mammalian cortex can, to a large extent, be predicted.

To achieve these results, a team from the Blue Brain Project set about virtually reconstructing a cortical microcircuit based on unparalleled data about the geometrical and electrical properties of neurons—data from over nearly 20 years of painstaking experimentation on slices of living brain tissue. Each neuron in the circuit was reconstructed into a 3D model on a powerful Blue Gene supercomputer. About 10,000 of virtual neurons were packed into a 3D space in random positions according to the density and ratio of morphological types found in corresponding living tissue. The researchers then compared the model back to an equivalent from a real mammalian brain.

A Major Step Towards Accurate Models of the Brain

This discovery also explains why the brain can withstand damage and indicates that the positions of synapses in all brains of the same species are more similar than different. "Positioning synapses in this way is very robust," says computational neuroscientist and first author Sean Hill, "We could vary density, position, orientation, and none of that changed the distribution of positions of the synapses."

They went on to discover that the synapses positions are only robust as long as the morphology of each neuron is slightly different from each other, explaining another mystery in the brain – why neurons are not all identical in shape. "It's the diversity in the morphology of neurons that makes brain circuits of a particular species basically the same and highly robust," says Hill.

Overall this work represents a major acceleration in the ability to construct detailed models of the nervous system. The results provide important insights into the basic principles that govern the wiring of the nervous system, throwing light on how robust cortical circuits are constructed from highly diverse populations of neurons – an essential step towards understanding how the brain functions. They also underscore the value of the BBP's constructivist approach. "Although systematically integrating data across a wide range of scales is slow and painstaking, it allows us to derive fundamental principles of structure and hence function," explains Hill.

Explore further: Scientists create first realistic 3D reconstruction of a brain circuit

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6 comments

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NeutronicallyRepulsive
3.5 / 5 (4) Sep 17, 2012
I really hope the Human Brain Project will be chosen by a FET Flagship Programme. This project can become really big quite soon. Interesting presentation by Henry Markram: http://www.youtub...H1Abuu9M
Parsec
2 / 5 (2) Sep 17, 2012
This is a profound discovery. For the first time, it points to the possibility of true AI, because it means the ability to create artificial brains of arbitrary complexity is a function much more dependent on increases in computer size, decreases in power consumption, etc. than precise connectivity.
GenesisNemesis
1 / 5 (1) Sep 18, 2012
Nevermind.
Torbjorn_Larsson_OM
not rated yet Sep 18, 2012
@ Parsec:

While I think it is an important step in predicting how evolution and development makes brains work, they emerge by large structures and then training, it seems to me to make AI *remoter*. Because such emergence is more difficult to understand and emulate.

Earlier it was recognized that the mind is embodied, signal processing is taking place remotely and by patterns rather than algorithms. (Say, making limbs move is training the pattern generators _and_ the limb processing.)

Now we learn that the brain is 'embodied' similarly.

If there is a saving grace, it is the old Church-Turing thesis. When we learn how to emulate a body with brain by hardware & software synthesis, we can start the larger project to emulate the hardware on software.

Eventually a pure AI is possible, I'm sure. But it will not come about by the simplistic "throw gates at it" Kurzweil woo pathway. His way is Artificial Dumbosity.
JVK
1 / 5 (1) Sep 18, 2012
Activity-dependent rewiring is driven by olfactory/pheromonal input that enables an organism with a CNS to maintain homeostasis. This attractive steady state is adaptively evolved via the epigenetic effects of nutrient chemicals and pheromones on ecological, social, neurogenic, and socio-cognitive niche construction as exemplified in the honeybee model organism which I used to show that "Olfaction and odor receptors provide a clear evolutionary trail that can be followed from unicellular organisms to insects to humans."

Isn't finding the "holy grail" that explains how information flows in the brain dependent on first recognizing how the epigenetic effects of nutrient chemicals and pheromones transfer information from the sensory environment via receptor-mediated events that alter intracellular signaling and stochastic gene expression during adaptive evolution? If so, I've found the holy grail when I wasn't even looking for it! http://dx.doi.org...i0.17338
Tausch
not rated yet Sep 22, 2012
Congratulations!

Is this what the 'gardeners' use to 'prune'?

http://medicalxpr...une.html

And your colleagues were soooo close!...
http://phys.org/n...954.html

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