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The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery.
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Rate, timing, and cooperativity jointly determine cortical synaptic plasticity
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Gabaergic circuits control spike-timing-dependent plasticity
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Endocannabinoid dynamics gate spike-timing dependent depression and potentiation
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Distributed circuit plasticity: new clues for the cerebellar mechanisms of learning
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Rl2: Fast reinforcement learning via slow reinforcement learning
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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
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Loihi: A neuromorphic manycore processor with on-chip learning
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Modulation of spike-timing dependent plasticity: towards the inclusion of a third factor in computational models
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Gerstner, W., Lehmann, M., Liakoni, V., Corneil, D., and Brea, J. (2018) · 2018
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Synaptic plasticity dynamics for deep continuous local learning
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Fully memristive neural networks for pattern classification with unsupervised learning
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Superspike: Supervised learning in multilayer spiking neural networks
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Bridging the gap between striatal plasticity and learning
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