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Found 10 papers, 2 papers with code
An Investigation of Conformal Isometry Hypothesis for Grid Cells
As the agent moves in the 2D physical space, the vector rotates in a 2D manifold in the neural space, driven by a recurrent neural network.
Emergence of Grid-like Representations by Training Recurrent Networks with Conformal Normalization
As the agent moves, the vector is transformed by an RNN that takes the velocity of the agent as input.
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells
Recurrent neural networks have been proposed to explain the properties of the grid cells by updating the neural activity vector based on the velocity input of the animal.
SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators
Recent advances in deep generative models have led to immense progress in 3D shape synthesis.
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details.
GenSample: A Genetic Algorithm for Oversampling in Imbalanced Datasets
In this paper, we present a novel technique based on genetic algorithms, GenSample, for oversampling the minority class in imbalanced datasets.
Bimodular continuous attractor neural networks with static and moving stimuli
We found that when bumps coexist in both modules, the position of each bump is shifted towards the other input when the intermodular couplings are excitatory and is shifted away when inhibitory.
Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection
In this paper, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors.
“Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation
Mimicking the experimental protocol, our model reproduces the characteristics of congruent and opposite neurons, and demonstrates that in each module, the sisters of congruent and opposite neurons can jointly achieve optimal multisensory information integration and segregation.
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively
Psychophysical experiments have demonstrated that the brain integrates information from multiple sensory cues in a near Bayesian optimal manner.
