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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.
A flow-based latent state generative model of neural population responses to natural images
We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations.
Generalization in data-driven models of primary visual cortex
With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network.
Learning From Brains How to Regularize Machines
We propose to regularize CNNs using large-scale neuroscience data to learn more robust neural features in terms of representational similarity.
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.
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data
Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution.
