What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?

DOI10.1162/0899766054796888zbMath1075.68635OpenAlexW1982580897WikidataQ51964612 ScholiaQ51964612MaRDI QIDQ5699302

Robert A. Legenstein, Christian Näger, Wolfgang Maass

Publication date: 26 October 2005

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/0899766054796888

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (22)

Fading memory and time series prediction in recurrent networks with different forms of plasticity ⋮ Adaptive Synchronization of Activities in a Recurrent Network ⋮ Supervised Learning in Multilayer Spiking Neural Networks ⋮ Associative memory of phase-coded spatiotemporal patterns in leaky integrate and fire networks ⋮ A basic compositional model for spiking neural networks ⋮ Improving a cortical pyramidal neuron model's classification performance on a real-world ECG dataset by extending inputs ⋮ A New Supervised Learning Algorithm for Spiking Neurons ⋮ Spike-Timing-Dependent Construction ⋮ Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions ⋮ Supervised learning with decision margins in pools of spiking neurons ⋮ Phenomenological models of synaptic plasticity based on spike timing ⋮ Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity ⋮ Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity ⋮ Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting ⋮ Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics ⋮ A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks ⋮ Spike-Timing Error Backpropagation in Theta Neuron Networks ⋮ A Theoretical Basis for Emergent Pattern Discrimination in Neural Systems Through Slow Feature Extraction ⋮ Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components ⋮ An online supervised learning method based on gradient descent for spiking neurons ⋮ Competitive STDP-Based Spike Pattern Learning ⋮ Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning

Cites Work

• Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns
• Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
• Activity-Dependent Development of Axonal and Dendritic Delays, or, Why Synaptic Transmission Should Be Unreliable

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