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A Computational Model of Learning Flexible Navigation in a Maze by Layout-Conforming Replay of Place Cells
First, we propose a Hebbian-like rule to learn the inter-PC synaptic strength during exploring a maze.
A Computational Theory of Learning Flexible Reward-Seeking Behavior with Place Cells
In this paper, we propose a computational theory that achieves behavioral flexibility with better biological plausibility.
Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization
It is critical to place operations in a neural network on these devices in an optimal way, so that the training process can complete within the shortest amount of time.
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Training deep neural networks (DNNs) requires an increasing amount of computation resources, and it becomes typical to use a mixture of GPU and CPU devices.
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