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Neuroscientists have long criticised deep learning algorithms as incompatible with current knowledge of neurobiology.
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How auto-encoders could provide credit assignment in deep networks via target propagation
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Deep generative stochastic networks trainable by backprop
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Generative adversarial networks
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Spike timing dependent plasticity
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Deep sparse rectifier neural networks
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Target propagation
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Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment
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Random feedback weights support learning in deep neural networks
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Stochastic variational learning in recurrent spiking networks
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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
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