Citation Relationships

Legends: Link to a Model Reference cited by multiple papers


Mozafari M, Kheradpisheh SR, Masquelier T, Nowzari-Dalini A, Ganjtabesh M (2018) First-Spike-Based Visual Categorization Using Reward-Modulated STDP IEEE Transactions on Neural Networks and Learning Systems :1-13

   First-Spike-Based Visual Categorization Using Reward-Modulated STDP (Mozafari et al. 2018)

References and models cited by this paper

References and models that cite this paper

Beyeler M,Dutt ND,Krichmar JL (2013) Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule Neural Netw. 48:109-124
Bi GQ, Poo MM (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18:10464-72 [PubMed]
Binas J,Indiveri G,Pfeiffer M (2016) Deep counter networks for asynchronous event-based processing https://arxiv.org/abs/1611.00710
Brader JM, Senn W, Fusi S (2007) Learning real-world stimuli in a neural network with spike-driven synaptic dynamics. Neural Comput 19:2881-912 [Journal] [PubMed]
Brette R (2012) Computing with neural synchrony. PLoS Comput Biol 8:e1002561 [Journal] [PubMed]
   Computing with neural synchrony (Brette 2012) [Model]
Courbariaux M,Bengio Y,David JP (2015) Binaryconnect: Training deep neural networks with binary weights during propagations Proc. Adv. Neural Inf. Process. Syst. :3123-3131
Daw ND, Doya K (2006) The computational neurobiology of learning and reward. Curr Opin Neurobiol 16:199-204 [Journal] [PubMed]
Dayan P, Balleine BW (2002) Reward, motivation, and reinforcement learning. Neuron 36:285-98 [PubMed]
DiCarlo JJ, Cox DD (2007) Untangling invariant object recognition. Trends Cogn Sci 11:333-41 [Journal] [PubMed]
DiCarlo JJ, Zoccolan D, Rust NC (2012) How does the brain solve visual object recognition? Neuron 73:415-34 [Journal] [PubMed]
Diehl PU, Cook M (2015) Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front Comput Neurosci 9:99 [Journal] [PubMed]
Diehl PU,Neil D,Binas J,Cook M,Liu SC,Pfeiffer M (2015) Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing Proc. Int. Joint Conf. Neural Netw. (IJCNN) :1-8
Farries MA, Fairhall AL (2007) Reinforcement learning with modulated spike timing dependent synaptic plasticity. J Neurophysiol 98:3648-65 [Journal] [PubMed]
Florian RV (2007) Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity. Neural Comput 19:1468-502 [Journal] [PubMed]
Frémaux N, Gerstner W (2015) Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules. Front Neural Circuits 9:85 [Journal] [PubMed]
Frémaux N, Sprekeler H, Gerstner W (2010) Functional requirements for reward-modulated spike-timing-dependent plasticity. J Neurosci 30:13326-37 [Journal] [PubMed]
Frémaux N, Sprekeler H, Gerstner W (2013) Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLoS Comput Biol 9:e1003024 [Journal] [PubMed]
Friedrich J, Urbanczik R, Senn W (2011) Spatio-temporal credit assignment in neuronal population learning. PLoS Comput Biol 7:e1002092 [Journal] [PubMed]
Fukushima K,Miyake S (1982) Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition Competition Cooperation Neural Nets :267-285
Furber S (2016) Large-scale neuromorphic computing systems. J Neural Eng 13:051001 [Journal] [PubMed]
Gardner B,Sporea I,Gru¨ning A (2014) Classifying spike patterns by reward-modulated STDP Proc. Int. Conf. Artif. Neural Netw. :749-756
Gerstner W, Kempter R, van Hemmen JL, Wagner H (1996) A neuronal learning rule for sub-millisecond temporal coding. Nature 383:76-81 [Journal] [PubMed]
Gilson M, Masquelier T, Hugues E (2011) STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Comput Biol 7:e1002231 [Journal] [PubMed]
   STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011) [Model]
Glimcher PW (2011) Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proc Natl Acad Sci U S A 108 Suppl 3:15647-54 [Journal] [PubMed]
Gu Q (2002) Neuromodulatory transmitter systems in the cortex and their role in cortical plasticity. Neuroscience 111:815-35 [PubMed]
Guo Y, Zhang W, Chen X, Fu J, Cheng W, Song D, Qu X, Yang Z, Zhao K (2017) Timing-dependent LTP and LTD in mouse primary visual cortex following different visual deprivation models. PLoS One 12:e0176603 [Journal] [PubMed]
Guyonneau R, VanRullen R, Thorpe SJ (2005) Neurons tune to the earliest spikes through STDP. Neural Comput 17:859-79 [Journal] [PubMed]
Hoerzer GM, Legenstein R, Maass W (2014) Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. Cereb Cortex 24:677-90 [Journal] [PubMed]
Huang S, Rozas C, Treviño M, Contreras J, Yang S, Song L, Yoshioka T, Lee HK, Kirkwood A (2014) Associative Hebbian synaptic plasticity in primate visual cortex. J Neurosci 34:7575-9 [Journal] [PubMed]
Huerta R, Nowotny T (2009) Fast and robust learning by reinforcement signals: explorations in the insect brain. Neural Comput 21:2123-51 [Journal] [PubMed]
Hung CP, Kreiman G, Poggio T, DiCarlo JJ (2005) Fast readout of object identity from macaque inferior temporal cortex. Science 310:863-6 [Journal] [PubMed]
Hussain S,Liu SC,Basu A (2014) Improved margin multi-class classification using dendritic neurons with morphological learning Proc. IEEE Int. Symp. Circuits Syst. (ISCAS) :2640-2643
Izhikevich EM (2007) Solving the distal reward problem through linkage of STDP and dopamine signaling. Cereb Cortex 17:2443-52 [Journal] [PubMed]
   Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007) [Model]
Kheradpisheh SR, Ganjtabesh M, Masquelier T (2016) Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition Neurocomputing 205:382-392
Kheradpisheh SR, Ganjtabesh M, Thorpe SJ, Masquelier T (2018) STDP-based spiking deep convolutional neural networks for object recognition. Neural Netw 99:56-67 [Journal] [PubMed]
Kheradpisheh SR, Ghodrati M, Ganjtabesh M, Masquelier T (2016) Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder. Front Comput Neurosci 10:92 [Journal] [PubMed]
Kheradpisheh SR, Ghodrati M, Ganjtabesh M, Masquelier T (2016) Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition. Sci Rep 6:32672 [Journal] [PubMed]
Krizhevsky A,Sutskever I,Hinton GE (2012) Imagenet classification with deep convolutional neural networks Advances in Neural Information Processing Systems, Pereira F:Burges C:Bottou L:Weinberger K, ed. pp.1097
Lecun (1998) Convolutional networks for images, speech, and time series The Handbook of Brain Theory and Neural Networks http://dl.acm.org/citation. cfm?id=303568.303704, Arbib MA, ed. pp.255
Lee D, Seo H, Jung MW (2012) Neural basis of reinforcement learning and decision making. Annu Rev Neurosci 35:287-308 [Journal] [PubMed]
Lee H, Grosse R, Ranganath R, Ng A (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations Proceedings of the 26th Annual International Conference on Machine Learning (ACM) :609-616
Lee JH, Delbruck T, Pfeiffer M (2016) Training Deep Spiking Neural Networks Using Backpropagation. Front Neurosci 10:508 [Journal] [PubMed]
Legenstein R, Pecevski D, Maass W (2008) A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback. PLoS Comput Biol 4:e1000180 [Journal] [PubMed]
   Reward modulated STDP (Legenstein et al. 2008) [Model]
Liu H, Agam Y, Madsen JR, Kreiman G (2009) Timing, timing, timing: fast decoding of object information from intracranial field potentials in human visual cortex. Neuron 62:281-90 [Journal] [PubMed]
Marder E (2012) Neuromodulation of neuronal circuits: back to the future. Neuron 76:1-11 [Journal] [PubMed]
Markram H, Lübke J, Frotscher M, Sakmann B (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275:213-5 [PubMed]
Masquelier T (2018) STDP Allows Close-to-Optimal Spatiotemporal Spike Pattern Detection by Single Coincidence Detector Neurons. Neuroscience 389:133-140 [Journal] [PubMed]
   Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017) [Model]
Masquelier T, Guyonneau R, Thorpe SJ (2008) Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS One 3:e1377 [Journal] [PubMed]
Masquelier T, Thorpe SJ (2007) Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Comput Biol 3:e31 [Journal] [PubMed]
Meliza CD, Dan Y (2006) Receptive-field modification in rat visual cortex induced by paired visual stimulation and single-cell spiking. Neuron 49:183-9 [Journal] [PubMed]
Merolla P,Arthur J,Akopyan F,Imam N,Manohar R,Modha DS (2011) A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45 nm Proc. IEEE Custom Integr. Circuits Conf. (CICC) :1-14
Nadim F, Bucher D (2014) Neuromodulation of neurons and synapses. Curr Opin Neurobiol 29:48-56 [Journal] [PubMed]
Neftci E, Das S, Pedroni B, Kreutz-Delgado K, Cauwenberghs G (2013) Event-driven contrastive divergence for spiking neuromorphic systems. Front Neurosci 7:272 [Journal] [PubMed]
Niv Y (2009) Reinforcement learning in the brain J. Math. Psychol. 53(3):139-154
O'Connor P, Neil D, Liu SC, Delbruck T, Pfeiffer M (2013) Real-time classification and sensor fusion with a spiking deep belief network. Front Neurosci 7:178 [Journal] [PubMed]
O'Connor P,Welling M (2016) Deep spiking networks https://arxiv.org/abs/1602.08323
Pavlov IP,Anrep GV (2003) Conditioned Reflexes
Ponulak F, Kasinski A (2010) Supervised learning in spiking neural networks with ReSuMe: sequence learning, classification, and spike shifting. Neural Comput 22:467-510 [Journal] [PubMed]
Querlioz D,Bichler O,Dollfus P,Gamrat C (2013) Immunity to device variations in a spiking neural network with memristive nanodevices IEEE Trans. Nanotechnol. 12(3):288-295
Reynolds JN, Wickens JR (2002) Dopamine-dependent plasticity of corticostriatal synapses. Neural Netw 15:507-21 [PubMed]
Rueckauer B, Lungu IA, Hu Y, Pfeiffer M, Liu SC (2017) Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification. Front Neurosci 11:682 [Journal] [PubMed]
Schultz W (1998) Predictive reward signal of dopamine neurons. J Neurophysiol 80:1-27 [Journal] [PubMed]
Schultz W (2002) Getting formal with dopamine and reward. Neuron 36:241-63 [PubMed]
Schultz W (2015) Neuronal Reward and Decision Signals: From Theories to Data. Physiol Rev 95:853-951 [Journal] [PubMed]
Seol GH, Ziburkus J, Huang S, Song L, Kim IT, Takamiya K, Huganir RL, Lee HK, Kirkwood A (2007) Neuromodulators control the polarity of spike-timing-dependent synaptic plasticity. Neuron 55:919-29 [Journal] [PubMed]
Serre T, Wolf L, Bileschi S, Riesenhuber M, Poggio T (2007) Robust object recognition with cortex-like mechanisms. IEEE Trans Pattern Anal Mach Intell 29:411-26 [Journal] [PubMed]
Simonyan K,Zisserman A (2014) Very deep convolutional networks for large-scale image recognition https://arxiv.org/abs/1409.1556
Sjöström PJ, Turrigiano GG, Nelson SB (2001) Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32:1149-64 [PubMed]
Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3:919-26 [Journal] [PubMed]
Steinberg EE, Keiflin R, Boivin JR, Witten IB, Deisseroth K, Janak PH (2013) A causal link between prediction errors, dopamine neurons and learning. Nat Neurosci 16:966-73 [Journal] [PubMed]
Sutton RS,Barto AG (1998) Reinforcement Learning (Adaptive Computation and Machine Learning)
Tavanaei A,Maida AS (2016) Bio-inspired spiking convolutional neural network using layer-wise sparse coding and STDP learning https://arxiv.org/abs/1611.03000
Tavanaei A,Masquelier T,Maida AS (2016) Acquisition of visual features through probabilistic spike-timing-dependent plasticity Proc. IEEE Int. Joint Conf. Neural Netw. (IJCNN) Jul:307-314
Thiele J,Diehl PU,Cook M (2017) A wake-sleep algorithm for recurrent, spiking neural networks https://arxiv.org/abs/1703.06290
Thorndike EL (1898) Review of animal intelligence: An experimental study of the associative processes in animals Psychol. Rev. 5(5):551-553
Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381:520-2 [Journal] [PubMed]
Thorpe S, Imbert M (1989) Biological constraints on connectionist modelling. Connectionism in Perspective., Pfeifer, ed.
VanRullen R, Thorpe SJ (2002) Surfing a spike wave down the ventral stream. Vision Res 42:2593-615 [PubMed]
Vasilaki E, Frémaux N, Urbanczik R, Senn W, Gerstner W (2009) Spike-based reinforcement learning in continuous state and action space: when policy gradient methods fail. PLoS Comput Biol 5:e1000586 [Journal] [PubMed]
Yu Q, Tang H, Tan KC, Li H (2013) Rapid feedforward computation by temporal encoding and learning with spiking neurons. IEEE Trans Neural Netw Learn Syst 24:1539-52 [Journal] [PubMed]
Zeiler MD,Fergus R (2014) Visualizing and understanding convolutional networks Proc. Eur. Conf. Comput. Vis. :818-833
Zhang JC, Lau PM, Bi GQ (2009) Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses. Proc Natl Acad Sci U S A 106:13028-33 [Journal] [PubMed]
Zhao B,Ding R,Chen S,Linares-Barranco B,Tang H (2015) Feedforward categorization on AER motion events using cortex-like features in a spiking neural network IEEE Trans. Neural Netw. Learn. Syst. 26(9):1963-1978
Masquelier T, Saeed Reza Kheradpisheh SR (2018) Optimal localist and distributed coding of spatiotemporal spike patterns through STDP and coincidence detection Front. Comput. Neurosci. [Journal]
   Optimal Localist and Distributed Coding Through STDP (Masquelier & Kheradpisheh 2018) [Model]
(87 refs)