Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity

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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. The paper describes a new biologically plausible mechanism for generating intermediate-level visual representations using an unsupervised learning scheme. These representations can then be used very effectively to perform categorization tasks using natural images. While the basic hierarchical architecture of the system is fairly similar to a number of other recent proposals, the key differences lie in the level of description that is used—individual neurons and spikes—and in the sort of coding scheme involved. Essentially, we have found that a combination of a temporal coding scheme where the most strongly activated neurons fire first with spike timing dependent plasticity leads to a situation where neurons in higher order visual areas will gradually become selective to frequently occurring feature combinations. At the same time, their responses become more and more rapid. We firmly believe that such mechanisms are a key to understanding the remarkable efficiency of the primate visual system.