A solution to the learning dilemma for recurrent networks of spiking neurons
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Abstract
Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unclear. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A mathematical result tells us how these pieces need to be combined to enable biologically plausible online network learning through gradient descent, in particular deep reinforcement learning. This learning method-called e-prop-approaches the performance of backpropagation through time (BPTT), the best-known method for training recurrent neural networks in…
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7Topics & keywords
Topics
Keywords
- Computer science
- Artificial intelligence
- Reinforcement learning
- Artificial neural network
- Spiking neural network
- Machine learning
- Spike (software development)
- Deep learning
UN Sustainable Development Goals
- Affordable and clean energy
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