Researchers at Israel's Hebrew University of Jerusalem and Harvard University explored the common octopus's neural makeup that defines its learning processes using automated tissue preparation and new machine learning reconstruction algorithms.
The researchers designed an algorithmically powered robotic system to cut and sort hundreds of ultrathin sections of the neural network within the octopus's vertical lobe, then assembled a three-dimensional model of the network's structural components.
They found the reconstruction replicated the animal's connectome, challenging entrenched assumptions about neural-network functionality related to learning and memory.
The researchers discovered the 25 million interneurons comprising the vertical lobe's network comprise simple amacrine cells and complex amacrine cells, which learn visual characteristics through synaptic reinforcement and consolidate activity levels, respectively.
From The Jerusalem Post (Israel)
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