6 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.NE2021
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.…
cs.NE2019★ 6 cited
A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction
Jielin Qiu, Ge Huang, Tai Sing Lee
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 t…
cs.CV2018★ 5 cited
Explaining Neural Networks Semantically and Quantitatively
Runjin Chen, Hao Chen, Ge Huang +2
This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically. The analysis of the specific rationale of eac…