DMCA
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity
Cached
Download Links
| Citations: | 60 - 6 self |
BibTeX
@MISC{Masquelier_unsupervisedlearning,
author = {Timothée Masquelier and Simon J. Thorpe},
title = {Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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.
Keyphrases
spike timing dependent plasticity visual feature learning rule intermediate-complexity visual feature emerges asynchronous feedforward natural image temporal code various classification task enable robust object recognition high synaptic weight prototypical pattern neural network selective response phenomenal processing speed dependent plasticity visual system relative timing synaptic strength postsynaptic spike ventral visual pathway postsynaptic spike latency decrease similar input
