Opening the black box of dendritic computing

phys.org | 3/7/2019 | Staff
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How do nerve cells compute? This fundamental question drives LMU neurobiologists led by Andreas Herz. They have now presented a novel method to disentangle complex neural processes in a much more powerful way than was previously possible.

We humans can feel, learn and perform purposeful acts only because the sensory information we receive from the environment is translated into nerve impulses, which are then processed in the brain. To accomplish this task, a single nerve cell in the cerebral cortex, to take only one example, may receive and process signals from thousands of other neurons. "In order to understand brain functions, it is absolutely vital to understand how nerve cells integrate these inputs," says Andreas Herz, Professor of Computational Neuroscience at LMU and a member of the Bernstein Center in Munich. He and his research group have now developed and applied a new analysis tool to characterize the computations carried out by individual neurons. In a new study published in PLoS Computational Biology, they report that the signal processing strongly depends on which part of the cell the synaptic input is delivered.

Operation - Nerve - Cell - Geometry - Properties

The specific mathematical operation carried out by a nerve cell is strongly influenced by the geometry and the biophysical properties of what is known as its dendritic tree. Dendrites are short cellular processes that extend from the nerve-cell body like the branches of a tree and harbor the functional contacts – known as synapses ¬– that receive signals from the axonal fibers of other nerve cells. Synaptic inputs are first integrated within each individual dendrite and are then combined with the results of other local computations, which together determine the response of the receiving neuron. "To analyze such complex processing cascades, researchers must activate multiple synaptic inputs simultaneously," as Stefan Häusler, who led the new study, points out. So far,...
(Excerpt) Read more at: phys.org
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