Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards.
Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse ...
Mar 18, 2020 · In this manuscript, we introduce a novel class of spiking neural network model that addresses the above issues with respect to partitioning the ...
Unsupervised Learning and Clustered Connectivity Enhance ... - NCBI
www.ncbi.nlm.nih.gov › PMC7970044
Mar 4, 2021 · We introduce a novel class of spiking neural network model, consisting of an input layer, a representation layer based on a balanced random ...
Mar 25, 2020 · This combination allows input features to be mapped to clusters; thus the network self-organizes to produce task-relevant activity patterns that ...
Mar 7, 2021 · Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse ...
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_, |a Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks |h online. 260, _, _, |a Lausanne |b Frontiers ...
Mar 19, 2020 · Unsupervised learning and clustered connectivity enhance reinforcement learning in spiking neural networks https://t.co/7LEHV7M8bB #bioRxiv.
[PDF] Investigation of Unsupervised Learning in Spiking Neural Network ...
isn.ucsd.edu › 2016_Group4
spiking neural network (SNN) is an increasing popular field. ... Since the training process is unsupervised, the cluster obtained from the training process needs ...