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Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

https://doi.org/10.1371/journal.pone.0161335
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Error-backpropagation in temporally encoded networks of spiking neurons
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Spiking Neuron Models
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First spikes in ensembles of human tactile afferents code complex spatial fingertip events
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The SpiNNaker Project
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Reliability of Spike Timing in Neocortical Neurons
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SPAN: SPIKE PATTERN ASSOCIATION NEURON FOR LEARNING SPATIO-TEMPORAL SPIKE PATTERNS
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Reinforcement Learning With Modulated Spike Timing–Dependent Synaptic Plasticity
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Precision of Spike Trains in Primate Retinal Ganglion Cells
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Response Variability and Timing Precision of Neuronal Spike Trains In Vivo
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Elman Backpropagation as Reinforcement for Simple Recurrent Networks
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Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting
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Supervised Learning in Multilayer Spiking Neural Networks
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Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks
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Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns
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Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type
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Matching Recall and Storage in Sequence Learning with Spiking Neural Networks
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Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity
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Stochastic variational learning in recurrent spiking networks
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Encoding Spike Patterns in Multilayer Spiking Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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