New study by Prof. Benny Hochner from the Alexander Silberman Institute of Life Sciences at The Hebrew University and Prof. Jeff Lichtman from Harvard University has unveiled the intricate neural architecture governing the enigmatic learning processes of Octopus vulgaris.
Collaboration with Prof. Jeff Lichtman's laboratory at Harvard University, leveraged innovative automated tissue preparation and new machine learning reconstruction algorithms. This cutting-edge technology enabled the cutting and sorting of ultra-thin sections, each a mere 30 millionths of a millimeter thick, constructing a three-dimensional representation of the structural elements comprising the network.
"We meticulously charted the connectivity of the vertical lobe using the precision of an electron microscope, achieving a resolution on the order of about 4 millionths of a millimeter. Together with Prof. Jeff Lichtman's team at Harvard University, we engineered a robotic system alongside a sophisticated computational algorithm, uniquely capable of organizing hundreds of ultra-thin sections into a comprehensive 3D structure.
This challenged established notions of neural network functionality in the context of learning and memory. Unlike typical models, the vertical lobe's network operates in a feed-forward configuration, like a one-way street, with information only from the input neurons to output neurons that control octopus behavior.
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