Maximum entropy inference on weight distributions under context-dependent task constraints produces neuron populations with contextual gain modulation whose connectivity matches gradient-descent trained networks, with transitions to random structure as context count or weight scale increases.
Lillicrap, Daniel Cownden, Douglas B
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EP-based PPO with CPG and residual policies matches standard PPO performance on 12-DoF quadruped uneven-terrain locomotion while using 4.3 times less GPU memory during training.
Modified feedback alignment in convolutional networks produces representations geometrically aligned with backpropagation on CIFAR-10.
citing papers explorer
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Balancing structure and randomness: maximum entropy networks for context-dependent computations
Maximum entropy inference on weight distributions under context-dependent task constraints produces neuron populations with contextual gain modulation whose connectivity matches gradient-descent trained networks, with transitions to random structure as context count or weight scale increases.
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Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain
EP-based PPO with CPG and residual policies matches standard PPO performance on 12-DoF quadruped uneven-terrain locomotion while using 4.3 times less GPU memory during training.
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Biological Plausibility and Representational Alignment of Feedback Alignment in Convolutional Networks
Modified feedback alignment in convolutional networks produces representations geometrically aligned with backpropagation on CIFAR-10.