The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks

Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

(2019) Ryan C. Williamson et al.   CURRENT OPINION IN NEUROBIOLOGY

Analyzing biological and artificial neural networks: challenges with opportunities for synergy?

(2019) David GT Barrett et al.   CURRENT OPINION IN NEUROBIOLOGY

Local online learning in recurrent networks with random feedback

(2019) James M Murray   eLife

Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks

(2019) Emre O. Neftci et al.   IEEE SIGNAL PROCESSING MAGAZINE

Towards spike-based machine intelligence with neuromorphic computing

(2019) Kaushik Roy et al.   NATURE

A deep learning framework for neuroscience

(2019) Blake A. Richards et al.   NATURE NEUROSCIENCE

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

(2018) Friedemann Zenke et al.   NEURAL COMPUTATION

Motor primitives in space and time via targeted gain modulation in cortical networks

(2018) Jake P. Stroud et al.   NATURE NEUROSCIENCE

'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification

(2018) Dean A Pospisil et al.   eLife

Deep Learning With Spiking Neurons: Opportunities and Challenges

(2018) Michael Pfeiffer et al.   Frontiers in Neuroscience

Flexible timing by temporal scaling of cortical responses

(2017) Jing Wang et al.   NATURE NEUROSCIENCE

Using goal-driven deep learning models to understand sensory cortex

(2016) Daniel L K Yamins et al.   NATURE NEUROSCIENCE

Convolutional networks for fast, energy-efficient neuromorphic computing

(2016) Steven K. Esser et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Spiking neurons can discover predictive features by aggregate-label learning

(2016) R. Gutig   SCIENCE

Training Deep Spiking Neural Networks Using Backpropagation

(2016) Jun Haeng Lee et al.   Frontiers in Neuroscience

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades

(2015) Garrick Orchard et al.   Frontiers in Neuroscience

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

Context-dependent computation by recurrent dynamics in prefrontal cortex

(2013) Valerio Mante et al.   NATURE

Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks

(2012) David Sussillo et al.   NEURAL COMPUTATION