What can a neuron learn with spike-timing-dependent plasticity?
about
Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequencesMixed signal learning by spike correlation propagation in feedback inhibitory circuits.Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.Computational aspects of feedback in neural circuitsSpatio-temporal credit assignment in neuronal population learning.Supervised learning with decision margins in pools of spiking neuronsThe chronotron: a neuron that learns to fire temporally precise spike patterns.Synaptic consolidation: an approach to long-term learningThe Convallis rule for unsupervised learning in cortical networks.Financial time series prediction using spiking neural networks.Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity RulesLearning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.Phenomenological models of synaptic plasticity based on spike timing.Spiking neural networks.Spike-timing-dependent construction.Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.On the relation between bursts and dynamic synapse properties: a modulation-based ansatz.Spike-Timing-dependent plasticity and short-term plasticity jointly control the excitation of Hebbian plasticity without weight constraints in neural networks.STDP in Recurrent Neuronal Networks.Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity.Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks.A new supervised learning algorithm for spiking neurons.Supervised learning in multilayer spiking neural networks.Random neuronal ensembles can inherently do context dependent coarse conjunctive encoding of input stimulus without any specific training.Competitive STDP-based spike pattern learning.Spike-timing error backpropagation in theta neuron networks.Learning real-world stimuli in a neural network with spike-driven synaptic dynamics.Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity.Reducing the variability of neural responses: a computational theory of spike-timing-dependent plasticity.Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting.Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning.Spiking neurons can learn to solve information bottleneck problems and extract independent components.Span: spike pattern association neuron for learning spatio-temporal spike patterns.A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction.Adaptive synchronization of activities in a recurrent network.Reinforcement learning with modulated spike timing dependent synaptic plasticity.A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques
P2860
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P2860
What can a neuron learn with spike-timing-dependent plasticity?
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What can a neuron learn with spike-timing-dependent plasticity?
@en
What can a neuron learn with spike-timing-dependent plasticity?
@nl
type
label
What can a neuron learn with spike-timing-dependent plasticity?
@en
What can a neuron learn with spike-timing-dependent plasticity?
@nl
prefLabel
What can a neuron learn with spike-timing-dependent plasticity?
@en
What can a neuron learn with spike-timing-dependent plasticity?
@nl
P2093
P2860
P356
P1433
P1476
What can a neuron learn with spike-timing-dependent plasticity?
@en
P2093
Christian Naeger
Robert Legenstein
Wolfgang Maass
P2860
P304
P356
10.1162/0899766054796888
P577
2005-11-01T00:00:00Z