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Learning Spike time codes through Morphological Learning with Binary Synapses
In this paper, a neuron with nonlinear dendrites (NNLD) and binary synapses that is able to learn temporal features of spike input patterns is considered.
Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites
This work also presents a branch-specific spike-based version of this structural plasticity rule.
Delay Learning Architectures for Memory and Classification
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns by changing the delays of every connection as opposed to modifying the weights.
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