Review on Methods to Fix Number of Hidden Neurons in Neural Networks DOI: 10 A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications, Biologically Inspired Cognitive Architectures, pp.49-64, 1155. ,
Unsupervised learning of digit recognition using spike-timing-dependent plasticity, Frontiers in Computational Neuroscience, vol.54, issue.178, p.99, 2015. ,
DOI : 10.1109/TNNLS.2014.2362542
URL : http://journal.frontiersin.org/article/10.3389/fncom.2015.00099/pdf
Learning a Spatially Smooth Subspace for Face Recognition, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007. ,
DOI : 10.1109/CVPR.2007.383054
URL : http://people.cs.uchicago.edu/%7Exiaofei/conference-9.pdf
Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2323, 1998. ,
DOI : 10.1109/5.726791
URL : http://www.cs.berkeley.edu/~daf/appsem/Handwriting/papers/00726791.pdf
Optimizing Number of Hidden Neurons in Neural Networks, 2007. ,
Selection of Proper Neural Network Sizes and Architectures???A Comparative Study, IEEE Transactions on Industrial Informatics, vol.8, issue.2, pp.228-240, 2012. ,
DOI : 10.1109/TII.2012.2187914
A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks, International Journal of Advanced Research in Artificial Intelligence, vol.4, issue.7, 2015. ,
DOI : 10.14569/IJARAI.2015.040701
URL : http://www.thesai.org/Downloads/IJARAI/Volume4No7/Paper_1-A_Minimal_Spiking_Neural_Network_to_Rapidly_Train.pdf
Simple model of spiking neurons, IEEE Neural Networks Council, pp.1569-1572, 2003. ,
DOI : 10.1109/TNN.2003.820440
URL : http://www.nsi.edu/users/izhikevich/publications/spikes.pdf
Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures, IEEE Transactions on Multi-Scale Computing Systems, vol.PP, issue.99, pp.1-1, 2017. ,
DOI : 10.1109/TMSCS.2017.2761231
URL : https://hal.archives-ouvertes.fr/hal-01615032
Memristor-The missing circuit element Circuit Theory, IEEE Transactions on, vol.18, pp.507-519, 1971. ,
DOI : 10.1109/tct.1971.1083337
The missing memristor found, Nature, vol.4, issue.7191, pp.80-83, 2008. ,
DOI : 10.1038/nature06932
Nanoscale Memristor Device as Synapse in Neuromorphic Systems, Nano Letters, vol.10, issue.4, pp.1297-1301, 2010. ,
DOI : 10.1021/nl904092h
URL : http://www.eecs.umich.edu/~wluee/LuJo_Synapse_NL2010.pdf
N2s3, a Simulator for the Architecture Exploration of Neuromorphic Accelerators, 2nd International Workshop on Neuromorphic and Brain-Based Computing Systems DATE Conference, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01240444
Neuromorphic electronic systems, Proceedings of the IEEE, vol.78, issue.10, pp.1629-1636, 1990. ,
DOI : 10.1109/5.58356
URL : http://www.ini.uzh.ch/~tobi/introneuroscience/meadNeuromorphicElectronicSystemsIEEE1990.pdf
A million spiking-neuron integrated circuit with a scalable communication network and interface, Science, vol.9, issue.2, pp.668-673, 2014. ,
DOI : 10.1007/BF00166411
The SpiNNaker Project, Proceedings of the IEEE, vol.102, issue.5, pp.652-665, 2014. ,
DOI : 10.1109/JPROC.2014.2304638
URL : http://doi.org/10.1109/jproc.2014.2304638
Nanoscale Memristor Device as Synapse in Neuromorphic Systems, Nano Letters, vol.10, issue.4, pp.1297-1301, 2010. ,
DOI : 10.1021/nl904092h
URL : http://www.eecs.umich.edu/~wluee/LuJo_Synapse_NL2010.pdf
Neuromorphic Computing Based on Emerging Memory Technologies, IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol.6, issue.2, pp.198-211, 2016. ,
DOI : 10.1109/JETCAS.2016.2533298
Development of a neuromorphic computing system, 2015 IEEE International Electron Devices Meeting (IEDM), pp.4-7, 2015. ,
DOI : 10.1109/IEDM.2015.7409624
Realtime classification and sensor fusion with a spiking deep belief network, Neuromorphic Engineering, vol.7, p.178, 2013. ,
Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations, Proceedings of the IEEE, pp.699-716, 2014. ,
DOI : 10.1109/JPROC.2014.2313565
Memristive switching mechanism for metal/oxide/metal nanodevices, Nature Nanotechnology, vol.49, issue.7, pp.429-433, 2008. ,
DOI : 10.1038/nnano.2008.160
Design implications of memristor-based rram cross-point structures, DATE, pp.734-739, 2011. ,
Training and operation of an integrated neuromorphic network based on metal-oxide memristors, Nature, vol.12, issue.2, pp.61-64, 2015. ,
DOI : 10.1007/978-3-642-35289-8_3
Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array, Frontiers in Neuroscience, vol.58, issue.186, p.205, 2014. ,
DOI : 10.1109/TED.2011.2147791
URL : http://journal.frontiersin.org/article/10.3389/fnins.2014.00205/pdf
Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses, Frontiers in Neuroscience, vol.60, issue.438, p.56, 2016. ,
DOI : 10.1109/TED.2013.2285403
URL : http://journal.frontiersin.org/article/10.3389/fnins.2016.00056/pdf
Controllable Growth of Nanoscale Conductive Filaments in Solid-Electrolyte-Based ReRAM by Using a Metal Nanocrystal Covered Bottom Electrode, ACS Nano, vol.4, issue.10, pp.6162-6168, 2010. ,
DOI : 10.1021/nn1017582
Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator, 2013 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), pp.7-12, 2013. ,
DOI : 10.1109/NanoArch.2013.6623029
Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses, Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses, pp.2402-2409, 2013. ,
DOI : 10.1109/TED.2013.2263000
URL : https://hal.archives-ouvertes.fr/hal-00871918
A ferroelectric memristor, Nature Materials, vol.83, issue.10, 2012. ,
DOI : 10.1063/1.1621731
Artificial synapses using ferroelectric memristors embedded with CMOS Circuit for image recognition, 72nd Device Research Conference, pp.297-298, 2014. ,
DOI : 10.1109/DRC.2014.6872414
A vlsi recurrent network of integrate-and-fire neurons connected by plastic synapses with long-term memory, IEEE Transactions on Neural Networks, vol.14, issue.5, pp.1297-1307, 2003. ,
DOI : 10.1109/TNN.2003.816367
Temporal Coding in a Silicon Network of Integrate-and-Fire Neurons, IEEE Transactions on Neural Networks, vol.15, issue.5, pp.1305-1314, 2004. ,
DOI : 10.1109/TNN.2004.832725
A quantitative description of membrane current and its application to conduction and excitation in nerve, The Journal of Physiology, vol.117, issue.4, pp.500-544, 1952. ,
DOI : 10.1113/jphysiol.1952.sp004764
Combining a Volatile and Nonvolatile Memristor in Artificial Synapse to Improve Learning in Spiking Neural Networks, 12th ACM/IEEE International Symposium on Nanoscale Architectures, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01368954
Unconventional digital computing approach: memristive nanodevice platform, physica status solidi (c), vol.12, issue.1-2, pp.222-228, 2015. ,
DOI : 10.1002/pssc.201400069
URL : https://hal.archives-ouvertes.fr/hal-01116577
Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns, Biological Cybernetics, vol.76, issue.5-6, pp.5-6, 1993. ,
DOI : 10.1007/978-3-642-69421-9_14
URL : http://infoscience.epfl.ch/record/97756/files/Gerstner_1993.pdf
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs, Science, vol.275, issue.5297, pp.213-215, 1997. ,
DOI : 10.1126/science.275.5297.213
STDP and STDP variations with memristors for spiking neuromorphic learning systems, Frontiers in Neuroscience, vol.7, issue.2, 2013. ,
DOI : 10.3389/fnins.2013.00002
URL : https://hal.archives-ouvertes.fr/hal-01578521
Stdp enables spiking neurons to detect hidden causes of their inputs, Advances in Neural Information Processing Systems 22, pp.1357-1365, 2009. ,
Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998. ,
DOI : 10.1109/5.726791
URL : http://www.cs.berkeley.edu/~daf/appsem/Handwriting/papers/00726791.pdf
Unsupervised learning of digit recognition using spike-timing-dependent plasticity, Frontiers in Computational Neuroscience, vol.54, issue.178, 2015. ,
DOI : 10.1109/TNNLS.2014.2362542
URL : http://journal.frontiersin.org/article/10.3389/fncom.2015.00099/pdf
NEURON ??? A Program for Simulation of Nerve Equations, Neural Systems: Analysis and Modeling (F. Eeckman, pp.127-136, 1993. ,
DOI : 10.1007/978-1-4615-3560-7_11
Brian: a simulator for spiking neural networks in python, Frontiers in Neuroinformatics, vol.2, issue.5, 2008. ,
NEST (NEural Simulation Tool), Scholarpedia, vol.2, issue.4, p.1430, 2007. ,
DOI : 10.4249/scholarpedia.1430
URL : https://doi.org/10.4249/scholarpedia.1430
Design exploration methodology for memristor-based spiking neuromorphic architectures with the Xnet event-driven simulator, 2013 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), pp.7-12, 2013. ,
DOI : 10.1109/NanoArch.2013.6623029