Blue Brain solves a century-old neuroscience problem

ScienceDaily | 3/21/2019 | Staff
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"For nearly 100 years, scientists have been trying to name cells. They have been describing them in the same way that Darwin described animals and trees. Now the Blue Brain Project has developed a mathematical algorithm to objectively classify the shapes of the neurons in the brain," explains Professor Henry Markram, Blue Brain's Founder and Director. "This will allow the development of a standardized taxonomy [classification of cells into distinct groups] of all cells in the brain, which will help researchers compare their data in a more reliable manner."

The team, with lead scientist Lida Kanari, have developed an algorithm to distinguish the different shapes of the most common type of neuron in the neocortex -- the pyramidal cells. Pyramidal cells are distinctively tree-like cells that make up 80% of the neurons in the neocortex and, like antennas, collect information from other neurons in the brain. Basically, they are the redwoods of the forests of trees in the brain. They are excitatory, sending waves of electrical activity through the network, as we perceive, act, and feel.

Father - Neuroscience - Ramón - Y - Cajal

The father of modern neuroscience, Ramón y Cajal, first drew pyramidal cells over 100 years ago, by looking at them under a microscope. Yet, up until now, scientists have not reached a consensus on the types of pyramidal neurons. Anatomists have been assigning names and debating the different types for the past century, while neuroscience has been unable to tell for sure which types of neurons are subjectively characterized. Even for visibly distinguishable neurons, there is no common ground to consistently define morphological types.

The study from Blue Brain proves for the first time that objective classification of these pyramidal cells is possible, by applying tools from algebraic topology, the branch of mathematics that studies the shape, connectivity, and the emergence of global structure from local constraints.

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(Excerpt) Read more at: ScienceDaily
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