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Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks
Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. To partition an environment into discrete states, implementations in spiking neuronal networks typically rely on input architectures involving place cell...
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| I publikationen: | Front Comput Neurosci |
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| Huvudupphovsmän: | , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
Frontiers Media S.A.
2021
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7970044/ https://ncbi.nlm.nih.gov/pubmed/33746728 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fncom.2021.543872 |
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