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A generic mechanism for perceptual organization in the parietal cortex. - PubMed - NCBI
"SIGNFICANCE STATEMENT Making sense of cluttered visual scenes is crucial for every-day perception. An important cue to scene segmentation is visual motion: slight movements of scene elements give away which elements belong to the fore- or background or to the same object. We used three distinct stimuli that engage visual scene segmentation mechanisms based on motion. They involved perceptual grouping, transparent motion and depth perception. Brain activity associated with all three mechanisms converged in the same parietal region with concurrent deactivation of early visual areas. The results suggest that posterior parietal cortex is a hub involved in structuring visual scenes based on different motion cues, and that feedback modulates early cortical processing in accord with predictive coding theory."
predictivecoding  predictiveprocessing  neuroscience  cognition  vision  movement 
18 hours ago by danhon
Google AI Blog: Improving Connectomics by an Order of Magnitude
The field of connectomics aims to comprehensively map the structure of the neuronal networks that are found in the nervous system, in order to better understand how the brain works. This process requires imaging brain tissue in 3D at nanometer resolution (typically using electron microscopy), and then analyzing the resulting image data to trace the brain’s neurites and identify individual synaptic connections. Due to the high resolution of the imaging, even a cubic millimeter of brain tissue can generate over 1,000 terabytes of data! When combined with the fact that the structures in these images can be extraordinarily subtle and complex, the primary bottleneck in brain mapping has been automating the interpretation of these data, rather than acquisition of the data itself.

Today, in collaboration with colleagues at the Max Planck Institute of Neurobiology, we published “High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks” in Nature Methods, which shows how a new type of recurrent neural network can improve the accuracy of automated interpretation of connectomics data by an order of magnitude over previous deep learning techniques. An open-access version of this work is also available from biorXiv (2017).
machine_learning  google  drosophila  neuroscience 
yesterday by amy
google/ffn: Flood-Filling Networks for instance segmentation in 3d volumes.
Flood-Filling Networks for instance segmentation in 3d volumes.

Flood-Filling Networks (FFNs) are a class of neural networks designed for instance segmentation of complex and large shapes, particularly in volume EM datasets of brain tissue.

For more details, see the related publications:
This is not an official Google product.
machine_learning  neuroscience  drosophila  google 
yesterday by amy
The Human Brain Can Create Structures in Up to 11 Dimensions
Last year, neuroscientists used a classic branch of maths in a totally new way to peer into the structure of our brains. What they discovered is that the brain is full of multi-dimensional geometrical structures operating in as many as 11 dimensions.
Archive  brain  neuroscience 
6 days ago by leninworld

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