The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity
Open accessAbstract
Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is repeatedly presented with similar inputs, STDP is known to have the effect of concentrating high synaptic weights on afferents that systematically fire early, while postsynaptic spike latencies decrease. Here we use this learning rule in an asynchronous feedforward spiking neural network that mimics the ventral visual pathway and shows that when the network is presented with natural images, selectivity to intermediate-complexity visual features emerges. Those features, which correspond to prototypical patterns that are both salient and consistently present in the images, are highly informative and enable robust object recognition, as demonstrated on various classification tasks. Taken together, these results show that temporal codes may be a key to understanding the phenomenal processing speed achieved by the visual system and that STDP can lead to fast and selective responses.
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Cites
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Speed of processing in the human visual system
Thorpe, S., Fize, D., & Marlot, C. (1996) · Nature · doi:10.1038/381520a0 · Cited by 3,538
Self-citationHierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
Vapnik, V. N., & Chervonenkis, A. Y. (1971) · Theory of Probability & Its Applications · doi:10.1137/1116025 · Cited by 2,448
Shape representation in the inferior temporal cortex of monkeys
Logothetis, N. K., Pauls, J., & Poggio, T. (1995) · Current Biology · doi:10.1016/s0960-9822(95)00108-4 · Cited by 963
Learning invariance from transformation sequences
Földiák, P. (1991) · Neural Computation · doi:10.1162/neco.1991.3.2.194 · Cited by 661
Visual features of intermediate complexity and their use in classification
Ullman, S., Vidal-Naquet, M., & Sali, E. (2002) · Nature Neuroscience · doi:10.1038/nn870 · Cited by 600
Cited by
HATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., & Benosman, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00186 · Cited by 486
Computing with Spiking Neuron Networks
Paugam-Moisy, H., & Bohte, S. (2012) · Handbook of Natural Computing · doi:10.1007/978-3-540-92910-9_10 · Cited by 272
Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses
Suri, M., Querlioz, D., Bichler, O., Palma, G., Vianello, E., Vuillaume, D., Gamrat, C., & DeSalvo, B. (2013) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2013.2263000 · Cited by 229
Tsanov, M., & Manahan-Vaughan, D. (2008) · The Neuroscientist · doi:10.1177/1073858408315655 · Cited by 65
Learning and disrupting invariance in visual recognition with a temporal association rule
Isik, L., Leibo, J. Z., & Poggio, T. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00037 · Cited by 38
Metadata
- Authors
- Masquelier, T., & Thorpe, S. J.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2007-02-16
- Volume
- 3
- Issue
- 2
- Pages
- e31
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2007815184
- Cited by
- 548
- References
- 7
- FWCI
- 6.19
- Type
- journal article
- Language
- en
- Open access
- gold
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