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。The Yin-Yang dataset
Laura Kriener et al.
PROCEEDINGS OF THE 2022 ANNUAL NEURO-INSPIRED COMPUTATIONAL ELEMENTS CONFERENCE (NICE 2022) (2022)
Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron
Saeed Reza Kheradpisheh et al.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS (2020)
TEMPORAL CODING IN SPIKING NEURAL NETWORKS WITH ALPHA SYNAPTIC FUNCTION
Julia M. Comsa et al.
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (2020)
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Timo Wunderlich et al.
FRONTIERS IN NEUROSCIENCE (2019)
A 4096-Neuron 1M-Synapse 3.8-pJ/SOP Spiking Neural Network With On-Chip STDP Learning and Sparse Weights in 10-nm FinFET CMOS
Gregory K. Chen et al.
IEEE JOURNAL OF SOLID-STATE CIRCUITS (2019)
All-optical spiking neurosynaptic networks with self-learning capabilities
J. Feldmann et al.
NATURE (2019)
Towards artificial general intelligence with hybrid Tianjic chip architecture
Jing Pei et al.
NATURE (2019)
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks
Emre O. Neftci et al.
IEEE SIGNAL PROCESSING MAGAZINE (2019)
Towards spike-based machine intelligence with neuromorphic computing
Kaushik Roy et al.
NATURE (2019)
A deep learning framework for neuroscience
Blake A. Richards et al.
NATURE NEUROSCIENCE (2019)
Biologically plausible deep learning - But how far can we go with shallow networks?
Bernd Illing et al.
NEURAL NETWORKS (2019)
Stochasticity from function - Why the Bayesian brain may need no noise
Dominik Dold et al.
NEURAL NETWORKS (2019)
Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks
Akos F. Kungl et al.
FRONTIERS IN NEUROSCIENCE (2019)
Deep learning in spiking neural networks
Amirhossein Tavanaei et al.
NEURAL NETWORKS (2019)
Deterministic networks for probabilistic computing
Jakob Jordan et al.
SCIENTIFIC REPORTS (2019)
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Mike Davies et al.
IEEE MICRO (2018)
A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Saber Moradi et al.
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS (2018)
An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture
Syed Ahmed Aamir et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS (2018)
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks
Friedemann Zenke et al.
NEURAL COMPUTATION (2018)
STDP-based spiking deep convolutional neural networks for object recognition
Saeed Reza Kheradpisheh et al.
NEURAL NETWORKS (2018)
Generalized leaky integrate-and-fire models classify multiple neuron types
Corinne Teeter et al.
NATURE COMMUNICATIONS (2018)
Spiking neurons with short-term synaptic plasticity form superior generative networks
Luziwei Leng et al.
SCIENTIFIC REPORTS (2018)
A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores
Syed Ahmed Aamir et al.
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS (2018)
Deep Learning With Spiking Neurons: Opportunities and Challenges
Michael Pfeiffer et al.
FRONTIERS IN NEUROSCIENCE (2018)
Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain
Chetan Singh Thakur et al.
FRONTIERS IN NEUROSCIENCE (2018)
Organic electronics for neuromorphic computing
Yoeri van De Burgt et al.
NATURE ELECTRONICS (2018)
Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System
Simon Friedmann et al.
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS (2017)
Mastering the game of Go without human knowledge
David Silver et al.
NATURE (2017)
Neuroscience-Inspired Artificial Intelligence
Demis Hassabis et al.
NEURON (2017)
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Emre O. Neftci et al.
FRONTIERS IN NEUROSCIENCE (2016)
Searching for principles of brain computation
Wolfgang Maass
CURRENT OPINION IN BEHAVIORAL SCIENCES (2016)
Stochastic inference with spiking neurons in the high-conductance state
Mihai A. Petrovici et al.
PHYSICAL REVIEW E (2016)
True North: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip
Filipp Akopyan et al.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS (2015)
Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition
Yongqiang Cao et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION (2015)
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
Mihai A. Petrovici et al.
PLOS ONE (2014)
Event-driven contrastive divergence for spiking neuromorphic systems
Emre Neftci et al.
FRONTIERS IN NEUROSCIENCE (2014)
Turing centenary: Is the brain a good model for machine intelligence?
NATURE (2012)
How Good Are Neuron Models?
Wulfram Gerstner et al.
SCIENCE (2009)
Rapid neural coding in the retina with relative spike latencies
Tim Gollisch et al.
SCIENCE (2008)
The tempotron:: a neuron that learns spike timing-based decisions
R Gütig et al.
NATURE NEUROSCIENCE (2006)
First spikes in ensembles of human tactile afferents code complex spatial fingertip events
RS Johansson et al.
NATURE NEUROSCIENCE (2004)
Which model to use for cortical spiking neurons?
EM Izhikevich
IEEE TRANSACTIONS ON NEURAL NETWORKS (2004)
Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents
A Rauch et al.
JOURNAL OF NEUROPHYSIOLOGY (2003)
Spike-based strategies for rapid processing
S Thorpe et al.
NEURAL NETWORKS (2001)
分享论文
