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Found 18 papers, 9 papers with code
Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors
We address this gap by proposing a probabilistic model that predicts the joint distribution of the neuronal responses from video stimuli and stimulus-independent latent factors.
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.
Reproducibility of predictive networks for mouse visual cortex
Such weight vectors, which can be thought as embeddings of neuronal function, have been proposed to define functional cell types via unsupervised clustering.
Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation
Current research has predominantly concentrated on generating multiple hypotheses for single frame static pose estimation or single hypothesis motion estimation.
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.
HARD: Hard Augmentations for Robust Distillation
To improve these aspects of KD, we propose Hard Augmentations for Robust Distillation (HARD), a generally applicable data augmentation framework, that generates synthetic data points for which the teacher and the student disagree.
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.
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.
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.
Factorized Neural Processes for Neural Processes: $K$-Shot Prediction of Neural Responses
We overcome this limitation by formulating the problem as $K$-shot prediction to directly infer a neuron's tuning function from a small set of stimulus-response pairs using a Neural Process.
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.
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.
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.
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes
On the other hand, layer normalization normalizes the activations across all activities within a layer.
Hierarchical Modeling of Local Image Features through L_p-Nested Symmetric Distributions
We introduce a new family of distributions, called $L_p${\em -nested symmetric distributions}, whose densities access the data exclusively through a hierarchical cascade of $L_p$-norms.
The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction
Bandpass filtering, orientation selectivity, and contrast gain control are prominent features of sensory coding at the level of V1 simple cells.
