4.6 Article

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights

Arash Samadi et al.

NEURAL COMPUTATION (2017)

Article Engineering, Electrical & Electronic

Learning in Silicon Beyond STDP: A Neuromorphic Implementation of Multi-Factor Synaptic Plasticity With Calcium-Based Dynamics

Frank L. Maldonado Huayaney et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS (2016)

Article Multidisciplinary Sciences

Convolutional networks for fast, energy-efficient neuromorphic computing

Steven K. Esser et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)

Review Behavioral Sciences

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated

Dharshan Kumaran et al.

TRENDS IN COGNITIVE SCIENCES (2016)

Article Multidisciplinary Sciences

Random synaptic feedback weights support error backpropagation for deep learning

Timothy P. Lillicrap et al.

NATURE COMMUNICATIONS (2016)

Article Neurosciences

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

Emre O. Neftci et al.

FRONTIERS IN NEUROSCIENCE (2016)

Article Neurosciences

Training Deep Spiking Neural Networks Using Backpropagation

Jun Haeng Lee et al.

FRONTIERS IN NEUROSCIENCE (2016)

Article Multidisciplinary Sciences

Human-level control through deep reinforcement learning

Volodymyr Mnih et al.

NATURE (2015)

Article Neurosciences

A framework for plasticity implementation on the SpiNNaker neural architecture

Francesco Galluppi et al.

FRONTIERS IN NEUROSCIENCE (2015)

Article Computer Science, Artificial Intelligence

Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition

Yongqiang Cao et al.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)

Article Neurosciences

Learning by the Dendritic Prediction of Somatic Spiking

Robert Urbanczik et al.

NEURON (2014)

Article Mathematical & Computational Biology

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

Friedemann Zenke et al.

FRONTIERS IN NEUROINFORMATICS (2014)

Article Neurosciences

Event-driven contrastive divergence for spiking neuromorphic systems

Emre Neftci et al.

FRONTIERS IN NEUROSCIENCE (2014)

Article Multidisciplinary Sciences

Synthesizing cognition in neuromorphic electronic systems

Emre Neftci et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2013)

Article Biochemical Research Methods

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

Bernhard Nessler et al.

PLOS COMPUTATIONAL BIOLOGY (2013)

Article Neurosciences

Real-time classification and sensor fusion with a spiking deep belief network

Peter O'Connor et al.

FRONTIERS IN NEUROSCIENCE (2013)

Article Multidisciplinary Sciences

Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location

Michael Graupner et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2012)

Article Neurosciences

Connectivity reflects coding: a model of voltage-based STDP with homeostasis

Claudia Clopath et al.

NATURE NEUROSCIENCE (2010)

Review Biochemical Research Methods

The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields

Gustavo Deco et al.

PLOS COMPUTATIONAL BIOLOGY (2008)

Article Computer Science, Artificial Intelligence

Learning real-world stimuli in a neural network with spike-driven synaptic dynamics

Joseph M. Brader et al.

NEURAL COMPUTATION (2007)

Article Computer Science, Artificial Intelligence

Spike-based strategies for rapid processing

S Thorpe et al.

NEURAL NETWORKS (2001)

Article Mathematical & Computational Biology

Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons

N Brunel

JOURNAL OF COMPUTATIONAL NEUROSCIENCE (2000)