about
Building functional networks of spiking model neurons.Modeling somatic and dendritic spike mediated plasticity at the single neuron and network levelSupervised learning in spiking neural networks with FORCE training.Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells.Computational Foundations of Natural Intelligence.SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition.Learning recurrent dynamics in spiking networks
P2860
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P2860
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
Learning Universal Computations with Spikes.
@ast
Learning Universal Computations with Spikes.
@en
type
label
Learning Universal Computations with Spikes.
@ast
Learning Universal Computations with Spikes.
@en
prefLabel
Learning Universal Computations with Spikes.
@ast
Learning Universal Computations with Spikes.
@en
P2093
P2860
P1476
Learning Universal Computations with Spikes.
@en
P2093
Dominik Thalmeier
Hilbert J Kappen
Marvin Uhlmann
Raoul-Martin Memmesheimer
P2860
P304
P356
10.1371/JOURNAL.PCBI.1004895
P577
2016-06-16T00:00:00Z
P698
P818
1505.07866