journal article Aug 03, 2015

Unsupervised learning of digit recognition using spike-timing-dependent plasticity

View at Publisher Save 10.3389/fncom.2015.00099
Topics

No keywords indexed for this article. Browse by subject →

References
49
[1]
Abbott "Temporally asymmetric hebbian learning, spike timing and neuronal response variability" Adv. Neural Inform. Process. Syst. (1999)
[2]
Azghadi "Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity" PLoS ONE (2014) 10.1371/journal.pone.0088326
[3]
Barroso "The price of performance" Queue (2005) 10.1145/1095408.1095420
[4]
Benjamin "Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations" Proc. IEEE (2014) 10.1109/jproc.2014.2313565
[5]
Beyeler "Categorization and decision-making in a neurobiologically plausible spiking network using a stdp-like learning rule" Neural Netw. (2013) 10.1016/j.neunet.2013.07.012
[6]
Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type

Guo-qiang Bi, Mu-ming Poo

The Journal of Neuroscience 1998 10.1523/jneurosci.18-24-10464.1998
[7]
Bichler "Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity" Neural Netw. (2012) 10.1016/j.neunet.2012.02.022
[8]
Brader "Learning real-world stimuli in a neural network with spike-driven synaptic dynamics" Neural Comput. (2007) 10.1162/neco.2007.19.11.2881
[9]
Coates "Learning feature representations with k-means" (2012) 10.1007/978-3-642-35289-8_30
[10]
Diehl "Efficient implementation of stdp rules on spinnaker neuromorphic hardware" (2014) 10.1109/ijcnn.2014.6889876
[11]
Diehl "Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing" (2015) 10.1109/ijcnn.2015.7280696
[12]
Fritzke "A growing neural gas network learns topologies" Adv. Neural Inform. Process. Syst. (1995)
[13]
Galluppi "A framework for plasticity implementation on the spinnaker neural architecture" Front. Neurosci. (2014) 10.3389/fnins.2014.00429
[14]
Goodhill "The role of weight normalization in competitive learning" Neural Comput. (1994) 10.1162/neco.1994.6.2.255
[15]
Goodman "Brian: a simulator for spiking neural networks in python" Front. Neuroinform. (2008) 10.3389/neuro.11.005.2008
[16]
Habenschuss "Homeostatic plasticity in bayesian spiking networks as expectation maximization with posterior constraints" (2012)
[17]
Reducing the Dimensionality of Data with Neural Networks

G. E. Hinton, R. R. Salakhutdinov

Science 2006 10.1126/science.1127647
[18]
Hussain "Improved margin multi-class classification using dendritic neurons with morphological learning" (2014) 10.1109/iscas.2014.6865715
[19]
Indiveri "A vlsi array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity" Neural Netw. IEEE Trans. (2006) 10.1109/tnn.2005.860850
[20]
Brain and high metabolic rate organ mass: contributions to resting energy expenditure beyond fat-free mass

Fahad Javed, Qing He, Lance E Davidson et al.

The American Journal of Clinical Nutrition 2010 10.3945/ajcn.2009.28512
[21]
Jug (2012)
[22]
Khan "Spinnaker: mapping neural networks onto a massively-parallel chip multiprocessor" (2008) 10.1109/ijcnn.2008.4634199
[23]
Kheradpisheh "Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition" arXiv (2015)
[24]
The self-organizing map

T. Kohonen

Proceedings of the IEEE 1990 10.1109/5.58325
[25]
Larochelle "Exploring strategies for training deep neural networks" J. Mach. Learn. Res. (2009)
[26]
Gradient-based learning applied to document recognition

Y. Lecun, L. Bottou, Y. Bengio et al.

Proceedings of the IEEE 1998 10.1109/5.726791
[27]
Leñero-Bardallo "A signed spatial contrast event spike retina chip" (2010) 10.1109/iscas.2010.5537152
[28]
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor

Patrick Lichtsteiner, Christoph Posch, Tobi Delbruck

IEEE Journal of Solid-State Circuits 2008 10.1109/jssc.2007.914337
[29]
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity

Timothée Masquelier, Simon J Thorpe

PLOS Computational Biology 2007 10.1371/journal.pcbi.0030031
[30]
Mayr "A biological-realtime neuromorphic system in 28 nm CMOS using low-leakage switched capacitor circuits" IEEE Trans. Biomed. Circuits Syst. (2015) 10.1109/tbcas.2014.2379294
[31]
McClelland "Parallel distributed processing" Explor. Microstruct. Cogn. (1986)
[32]
Merolla "A digital neurosynaptic core using embedded crossbar memory with 45pj per spike in 45nm" (2011) 10.1109/cicc.2011.6055294
[33]
A million spiking-neuron integrated circuit with a scalable communication network and interface

Paul A. Merolla, John V. Arthur, Rodrigo Alvarez-Icaza et al.

Science 2014 10.1126/science.1254642
[34]
Morrison "Spike-timing-dependent plasticity in balanced random networks" Neural Comput. (2007) 10.1162/neco.2007.19.6.1437
[35]
Neftci "Event-driven contrastive divergence for spiking neuromorphic systems" Front. Neurosci. (2013) 10.3389/fnins.2013.00272
[36]
Neil "Minitaur, an event-driven fpga-based spiking network accelerator" Very Large Scale Int. Syst. IEEE Trans. (2014) 10.1109/tvlsi.2013.2294916
[37]
Nessler "Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity" PLoS Comput. Biol. (2013) 10.1371/journal.pcbi.1003037
[38]
O'Connor "Real-time classification and sensor fusion with a spiking deep belief network" Front. Neurosci. (2013) 10.3389/fnins.2013.00178
[39]
Computational Explorations in Cognitive Neuroscience

Randall C. O'Reilly, Yuko Munakata

2000 10.7551/mitpress/2014.001.0001
[40]
Park "A 65k-neuron 73-mevents/s 22-pj/event asynchronous micro-pipelined integrate-and-fire array transceiver" (2014) 10.1109/biocas.2014.6981816
[41]
Pfister "Triplets of spikes in a model of spike timing-dependent plasticity" J. Neurosci. (2006) 10.1523/jneurosci.1425-06.2006
[42]
Posch "High-dr frame-free pwm imaging with asynchronous aer intensity encoding and focal-plane temporal redundancy suppression" (2010) 10.1109/iscas.2010.5537150
[43]
Querlioz "Immunity to device variations in a spiking neural network with memristive nanodevices" Nanotechnol. IEEE Trans. (2013) 10.1109/tnano.2013.2250995
[44]
Querlioz "Simulation of a memristor-based spiking neural network immune to device variations" (2011) 10.1109/ijcnn.2011.6033439
[45]
Querlioz "Learning with memristive devices: How should we model their behavior?" (2011) 10.1109/nanoarch.2011.5941497
[46]
Rahimi Azghadi "Spike-based synaptic plasticity in silicon: Design, implementation, application, and challenges" Proc. IEEE (2014) 10.1109/jproc.2014.2314454
[47]
Rumelhart "Learning internal representations by error propagation" (1985) 10.21236/ada164453
[48]
Turrigiano "Homeostatic plasticity in the developing nervous system" Nat. Rev. Neurosci. (2004) 10.1038/nrn1327
[49]
Zhao "Feedforward categorization on AER motion events using cortex-like features in a spiking neural network" IEEE Trans. Neural Netw. Learn. Sys. (2014) 10.1109/tnnls.2014.2362542
Metrics
1,209
Citations
49
References
Details
Published
Aug 03, 2015
Vol/Issue
9
Cite This Article
Peter U. Diehl, Matthew Cook (2015). Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Frontiers in Computational Neuroscience, 9. https://doi.org/10.3389/fncom.2015.00099