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Found 6 papers, 5 papers with code
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice.
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
We evaluate cGNF on the Human~3. 6M dataset and show that cGNF provides a well-calibrated distribution estimate while being close to state-of-the-art in terms of overall minMPJPE.
Density Estimation
Multi-Hypotheses 3D Human Pose Estimation
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.
