Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks

DOI10.1162/NECO_a_00790zbMath1414.92019arXiv1503.09129WikidataQ40398877 ScholiaQ40398877MaRDI QIDQ5380354

André Grüning, Brian Gardner, Ioana Sporea

Publication date: 4 June 2019

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

Full work available at URL: https://arxiv.org/abs/1503.09129

zbMATH Keywords

learning rulespiking neural networksspatiotemporally encoded pattern transformations

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

Cites Work

• The evidence for neural information processing with precise spike-times: A survey
• Error-backpropagation in temporally encoded networks of spiking neurons
• Predicting spike timing of neocortical pyramidal neurons by simple threshold models
• A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
• A Novel Spike Distance
• Learning Spike-Based Population Codes by Reward and Population Feedback
• Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity
• Spike-Timing Error Backpropagation in Theta Neuron Networks
• Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
• Spiking Neuron Models
• Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting
• Supervised Learning in Multilayer Spiking Neural Networks
• Elman Backpropagation as Reinforcement for Simple Recurrent Networks
• Polychronization: Computation with Spikes
• Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning