Classifying Spike Patterns by Reward-Modulated STDP

Gardner, Brian; Sporea, Ioana; Grüning, André

doi:10.1007/978-3-319-11179-7_94

Cited by 1 publication

(1 citation statement)

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“…To date, we could not find any other works possessing the aforementioned features. To mention one of the closest attempts, Gardner et al [84] tried to classify Poisson-distributed spike trains by a readout neuron equipped with R-STDP. Although their method is working, it cannot be applied on natural images as it is, because of their time-based encoding and target labeling.…”

Section: Discussionmentioning

confidence: 99%

First-Spike-Based Visual Categorization Using Reward-Modulated STDP

Mozafari

Kheradpisheh

Masquelier³

et al. 2018

IEEE Trans. Neural Netw. Learning Syst.

150

127

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Reinforcement learning (RL) has recently regained popularity with major achievements such as beating the European game of Go champion. Here, for the first time, we show that RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier. We used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. In the highest layers, each neuron was assigned to an object category, and it was assumed that the stimulus category was the category of the first neuron to fire. If this assumption was correct, the neuron was rewarded, i.e., spike-timing-dependent plasticity (STDP) was applied, which reinforced the neuron's selectivity. Otherwise, anti-STDP was applied, which encouraged the neuron to learn something else. As demonstrated on various image data sets (Caltech, ETH-80, and NORB), this reward-modulated STDP (R-STDP) approach has extracted particularly discriminative visual features, whereas classic unsupervised STDP extracts any feature that consistently repeats. As a result, R-STDP has outperformed STDP on these data sets. Furthermore, R-STDP is suitable for online learning and can adapt to drastic changes such as label permutations. Finally, it is worth mentioning that both feature extraction and classification were done with spikes, using at most one spike per neuron. Thus, the network is hardware friendly and energy efficient.

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Section: Discussionmentioning

confidence: 99%