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Found 5 papers, 0 papers with code
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.
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.
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.
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.
Explaining Neural Networks Semantically and Quantitatively
This paper presents a method to explain the knowledge encoded in a convolutional neural network (CNN) quantitatively and semantically.
