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Found 6 papers, 4 papers with code
The Sensorium competition on predicting large-scale mouse primary visual cortex activity
The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input.
Towards robust vision by multi-task learning on monkey visual cortex
We found that our co-trained network is more sensitive to content than noise when compared to a Baseline network that we trained for image classification alone.
Rotation-invariant clustering of neuronal responses in primary visual cortex
Similar to a convolutional neural network (CNN), the mammalian retina encodes visual information into several dozen nonlinear feature maps, each formed by one ganglion cell type that tiles the visual space in an approximately shift-equivariant manner.
How well do deep neural networks trained on object recognition characterize the mouse visual system?
Recent work on modeling neural responses in the primate visual system has benefited from deep neural networks trained on large-scale object recognition, and found a hierarchical correspondence between layers of the artificial neural network and brain areas along the ventral visual stream.
A rotation-equivariant convolutional neural network model of primary visual cortex
We present a framework to identify common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations.
Diverse feature visualizations reveal invariances in early layers of deep neural networks
Here we propose a method to discover invariances in the responses of hidden layer units of deep neural networks.
