Learning spike time codes through supervised and unsupervised structural plasticity
Large-scale spiking neural networks (SNN) are typically implemented on the chip by using mixed analog-digital circuits. While the models of the network components (neurons, synapses, and dendrites) are implemented by analog VLSI techniques, the connectivity information of the network is stored in an...
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| Format: | Thesis |
| Language: | English |
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2016
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| Online Access: | https://hdl.handle.net/10356/67327 |