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Found 17 papers, 3 papers with code
Does resistance to style-transfer equal Global Shape Bias? Measuring network sensitivity to global shape configuration
The current benchmark for evaluating a model's global shape bias is a set of style-transferred images with the assumption that resistance to the attack of style transfer is related to the development of global structure sensitivity in the model.
A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes
To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses.
Relating Human Perception of Musicality to Prediction in a Predictive Coding Model
Our findings suggest that predictability is correlated with human perception of musicality and that a predictive coding neural network trained on music can be used to characterize the features and motifs contributing to human perception of music.
SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators
Recent advances in deep generative models have led to immense progress in 3D shape synthesis.
Recurrent networks improve neural response prediction and provide insights into underlying cortical circuits
Feedforward CNN models have proven themselves in recent years as state-of-the-art models for predicting single-neuron responses to natural images in early visual cortical neurons.
Prototype memory and attention mechanisms for few shot image generation
To test our proposal, we show in a few-shot image generation task, that having a prototype memory during attention can improve image synthesis quality, learn interpretable visual concept clusters, as well as improve the robustness of the model.
Recurrent Feedback Improves Feedforward Representations in Deep Neural Networks
The abundant recurrent horizontal and feedback connections in the primate visual cortex are thought to play an important role in bringing global and semantic contextual information to early visual areas during perceptual inference, helping to resolve local ambiguity and fill in missing details.
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits
This study provides a normative theory for how Bayesian causal inference can be implemented in neural circuits.
Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex
The model is a hierarchical recurrent neural model that learns to predict video sequences using the incoming video signals as teaching signals.
Complexity and Diversity in Sparse Code Priors Improve Receptive Field Characterization of Macaque V1 Neurons
System identification techniques -- projection pursuit regression models (PPRs) and convolutional neural networks (CNNs) -- provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli.
A Model Cortical Network for Spatiotemporal Sequence Learning and Prediction
In this paper we developed a hierarchical network model, called Hierarchical Prediction Network (HPNet) to understand how spatiotemporal memories might be learned and encoded in a representational hierarchy for predicting future video frames.
Contextual Recurrent Convolutional Model for Robust Visual Learning
In this work, we designed a Contextual Recurrent Convolutional Network with this feature embedded in a standard CNN structure.
A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction
Within each level, the feed-forward path and the feedback path intersect in a recurrent gated circuit, instantiated in a LSTM module, to generate a prediction or explanation of the incoming signals.
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space.
Learning to Associate Words and Images Using a Large-scale Graph
In this particular problem, a user is presented with a deformed picture of a Chinese phrase and eight low-resolution images.
Transfer of View-manifold Learning to Similarity Perception of Novel Objects
We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience.
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework.
