SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

Robust learning in SpikeProp

(2017) Sumit Bam Shrestha et al.   NEURAL NETWORKS

Towards deep learning with segregated dendrites

(2017) Jordan Guerguiev et al.   eLife

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

(2017) Aditya Gilra et al.   eLife

Building functional networks of spiking model neurons

(2016) L F Abbott et al.   NATURE NEUROSCIENCE

Efficient codes and balanced networks

(2016) Sophie Denève et al.   NATURE NEUROSCIENCE

Learning Precise Spike Train–to–Spike Train Transformations in Multilayer Feedforward Neuronal Networks

(2016) Arunava Banerjee   NEURAL COMPUTATION

On the Analytical Solution of Firing Time for SpikeProp

(2016) Simon de Montigny et al.   NEURAL COMPUTATION

Spiking neurons can discover predictive features by aggregate-label learning

(2016) R. Gutig   SCIENCE

Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

(2016) Nicolas Frémaux et al.   Frontiers in Neural Circuits

Toward an Integration of Deep Learning and Neuroscience

(2016) Adam H. Marblestone et al.   Frontiers in Computational Neuroscience

Representational Distance Learning for Deep Neural Networks

(2016) Patrick McClure et al.   Frontiers in Computational Neuroscience

Random synaptic feedback weights support error backpropagation for deep learning

(2016) Timothy P. Lillicrap et al.   Nature Communications

Learning Universal Computations with Spikes

(2016) Dominik Thalmeier et al.   PLoS Computational Biology

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

(2016) Christian Albers et al.   PLoS One

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

(2016) Brian Gardner et al.   PLoS One

Deep learning

(2015) Yann LeCun et al.   NATURE

Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks

(2015) Brian Gardner et al.   NEURAL COMPUTATION

Adaptive learning rate of SpikeProp based on weight convergence analysis

(2015) Sumit Bam Shrestha et al.   NEURAL NETWORKS

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Unsupervised learning of digit recognition using spike-timing-dependent plasticity

(2015) Peter U. Diehl et al.   Frontiers in Computational Neuroscience

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

(2015) Friedemann Zenke et al.   Nature Communications

Learning Precisely Timed Spikes

(2014) Raoul-Martin Memmesheimer et al.   NEURON

Performance-optimized hierarchical models predict neural responses in higher visual cortex

(2014) D. L. K. Yamins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Stochastic variational learning in recurrent spiking networks

(2014) Danilo Jimenez Rezende et al.   Frontiers in Computational Neuroscience

Limits to high-speed simulations of spiking neural networks using general-purpose computers

(2014) Friedemann Zenke et al.   Frontiers in Neuroinformatics

Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

(2013) J. Brea et al.   JOURNAL OF NEUROSCIENCE

SPAN: SPIKE PATTERN ASSOCIATION NEURON FOR LEARNING SPATIO-TEMPORAL SPIKE PATTERNS

(2012) AMMAR MOHEMMED et al.   International Journal of Neural Systems

Supervised Learning in Multilayer Spiking Neural Networks

(2012) Ioana Sporea et al.   NEURAL COMPUTATION

The Spike-Timing Dependence of Plasticity

(2012) Daniel E. Feldman   NEURON

The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns

(2012) Răzvan V. Florian   PLoS One

A Large-Scale Model of the Functioning Brain

(2012) C. Eliasmith et al.   SCIENCE

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

(2010) N. Fremaux et al.   JOURNAL OF NEUROSCIENCE

Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting

(2009) Filip Ponulak et al.   NEURAL COMPUTATION

A Gradient Learning Rule for the Tempotron

(2008) Robert Urbanczik et al.   NEURAL COMPUTATION

A learning rule for very simple universal approximators consisting of a single layer of perceptrons

(2008) Peter Auer et al.   NEURAL NETWORKS

Rapid Neural Coding in the Retina with Relative Spike Latencies

(2008) T. Gollisch et al.   SCIENCE