STDP-based spiking deep neural networks for object recognition

[1]  Refractor,et al.  Third webspace to thumb digital nerve transfer for traumatic avulsion injury , 2023, The Journal of hand surgery, European volume.

[2]  F. Pelayo,et al.  A Computational Framework for Realistic Retina Modeling , 2016, Int. J. Neural Syst..

[3]  Antonio Torralba,et al.  Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.

[4]  Pierre Kornprobst,et al.  Rank Order Coding: a Retinal Information Decoding Strategy Revealed by Large-Scale Multielectrode Array Retinal Recordings123 , 2016, eNeuro.

[5]  Timothée Masquelier,et al.  Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder , 2016, Front. Comput. Neurosci..

[6]  Kaushik Roy,et al.  Unsupervised regenerative learning of hierarchical features in Spiking Deep Networks for object recognition , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[7]  Andrew S. Cassidy,et al.  Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware , 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

[8]  Kendra S. Burbank.  Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons , 2015, PLoS Comput. Biol..

[9]  Chris Eliasmith,et al.  Spiking Deep Networks with LIF Neurons , 2015, ArXiv.

[10]  Bernabé Linares-Barranco,et al.  Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[11]  Timothée Masquelier,et al.  Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition , 2015, Scientific Reports.

[12]  Matthew Cook,et al.  Unsupervised learning of digit recognition using spike-timing-dependent plasticity , 2015, Front. Comput. Neurosci..

[13]  Matthew Cook,et al.  Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[14]  Bernabé Linares-Barranco,et al.  Fast Pipeline 128×128 pixel spiking convolution core for event-driven vision processing in FPGAs , 2015, 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP).

[15]  A. Bonci,et al.  Role of Dopamine Neurons in Reward and Aversion: A Synaptic Plasticity Perspective , 2015, Neuron.

[16]  Yongqiang Cao,et al.  Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition , 2015, International Journal of Computer Vision.

[17]  Timothée Masquelier,et al.  Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition , 2015, Neurocomputing.

[18]  Yang Yang,et al.  Supervised feature learning via l2-norm regularized logistic regression for 3D object recognition , 2015, Neurocomputing.

[19]  Yoshua Bengio,et al.  Towards Biologically Plausible Deep Learning , 2015, ArXiv.

[20]  Nikolaus Kriegeskorte,et al.  Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..

[21]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[22]  Reza Ebrahimpour,et al.  Feedforward object-vision models only tolerate small image variations compared to human , 2014, Front. Comput. Neurosci..

[23]  Daniel L. K. Yamins,et al.  Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..

[24]  Shaista Hussain,et al.  Improved margin multi-class classification using dendritic neurons with morphological learning , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[25]  A. Kirkwood,et al.  Associative Hebbian Synaptic Plasticity in Primate Visual Cortex , 2014, The Journal of Neuroscience.

[26]  Nikil D. Dutt,et al.  Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule , 2013, Neural Networks.

[27]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[28]  Tobi Delbruck,et al.  Real-time classification and sensor fusion with a spiking deep belief network , 2013, Front. Neurosci..

[29]  D. Querlioz,et al.  Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices , 2013, IEEE Transactions on Nanotechnology.

[30]  T. Serrano-Gotarredona,et al.  STDP and STDP variations with memristors for spiking neuromorphic learning systems , 2013, Front. Neurosci..

[31]  Stefan Habenschuss,et al.  Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints , 2012, NIPS.

[32]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[33]  H. R. González.  Computing with Spikes , 2012 .

[34]  D. Leopold,et al.  Stimulus Timing-Dependent Plasticity in High-Level Vision , 2012, Current Biology.

[35]  Nicole C. Rust,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[36]  Nicolas Pinto,et al.  Comparing state-of-the-art visual features on invariant object recognition tasks , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[37]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[38]  G. Kreiman,et al.  Timing, Timing, Timing: Fast Decoding of Object Information from Intracranial Field Potentials in Human Visual Cortex , 2009, Neuron.

[39]  Pierre Kornprobst,et al.  Virtual Retina: A biological retina model and simulator, with contrast gain control , 2009, Journal of Computational Neuroscience.

[40]  Tobi Delbrück,et al.  A 128$\times$ 128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor , 2008, IEEE Journal of Solid-State Circuits.

[41]  Walter Senn,et al.  Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics , 2007, Neural Computation.

[42]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[43]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Timothée Masquelier,et al.  Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..

[45]  Simon J. Thorpe,et al.  Ultra-rapid object detection with saccadic eye movements: Visual processing speed revisited , 2006, Vision Research.

[46]  R. Segev,et al.  How silent is the brain: is there a “dark matter” problem in neuroscience? , 2006, Journal of Comparative Physiology A.

[47]  Máté Lengyel,et al.  Computing with spikes , 2006 .

[48]  Y. Dan,et al.  Receptive-Field Modification in Rat Visual Cortex Induced by Paired Visual Stimulation and Single-Cell Spiking , 2006, Neuron.

[49]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[50]  S. Thorpe,et al.  Taking the MAX from neuronal responses , 2003, Trends in Cognitive Sciences.

[51]  Gustavo Deco,et al.  Computational neuroscience of vision , 2002 .

[52]  Arnaud Delorme,et al.  Spike-based strategies for rapid processing , 2001, Neural Networks.

[53]  Arnaud Delorme,et al.  Networks of integrate-and-fire neurons using Rank Order Coding B: Spike timing dependent plasticity and emergence of orientation selectivity , 2001, Neurocomputing.

[54]  Rufin van Rullen,et al.  Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex , 2001, Neural Computation.

[55]  K. Doya.  Complementary roles of basal ganglia and cerebellum in learning and motor control , 2000, Current Opinion in Neurobiology.

[56]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[57]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[58]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[59]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[60]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[61]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[62]  Massimo A. Sivilotti,et al.  Wiring considerations in analog VLSI systems, with application to field-programmable networks , 1992 .

[63]  David D. Cox,et al.  Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .