7 citations · 16 across the 5 of their papers we have counts for
5 papers
Recurrent networks improve neural response prediction and provide insights into underlying cortical circuits
Yimeng Zhang, Harold Rockwell, Sicheng Dai +4
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.…
Recurrent Feedback Improves Feedforward Representations in Deep Neural Networks
Siming Yan, Xuyang Fang, Bowen Xiao +3
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…
Complexity and Diversity in Sparse Code Priors Improve Receptive Field Characterization of Macaque V1 Neurons
Ziniu Wu, Harold Rockwell, Yimeng Zhang +2
System identification techniques -- projection pursuit regression models (PPRs) and convolutional neural networks (CNNs) -- provide state-of-the-art performance in predicting visua…
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Hao Wang, Xingyu Lin, Yimeng Zhang +1
Recurrent feedback connections in the mammalian visual system have been hypothesized to play a role in synthesizing input in the theoretical framework of analysis by synthesis. The…
Transfer of View-manifold Learning to Similarity Perception of Novel Objects
Xingyu Lin, Hao Wang, Zhihao Li +3
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 cap…