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Found 4 papers, 1 papers with code
Spiking Network Initialisation and Firing Rate Collapse
We show that the problem of weight initialisation for ANNs is a more nuanced problem than it is for ANNs due to the spike-and-reset non-linearity of SNNs and the firing rate collapse problem.
Human-Timescale Adaptation in an Open-Ended Task Space
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL).
Sparse Spiking Gradient Descent
There is an increasing interest in emulating Spiking Neural Networks (SNNs) on neuromorphic computing devices due to their low energy consumption.
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Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
