ArticleJournal of computational neuroscience2016
Slow feature analysis with spiking neurons and its application to audio stimuli.
Article in Journal of computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Slow and steady: auditory features for discriminating animal vocalizations.bioRxiv : the preprint server for biology · 2024Article
- Time as a supervisor: temporal regularity and auditory object learning.Frontiers in computational neuroscience · 2023Article
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Authors and funding
4 authors.
Funding
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Abstract
Extracting invariant features in an unsupervised manner is crucial to perform complex computation such as object recognition, analyzing music or understanding speech. While various algorithms have been proposed to perform such a task, Slow Feature Analysis (SFA) uses time as a means of detecting those invariants, extracting the slowly time-varying components in the input signals. In this work, we address the question of how such an algorithm can be implemented by neurons, and apply it in the context of audio stimuli. We propose a projected gradient implementation of SFA that can be adapted to a Hebbian like learning rule dealing with biologically plausible neuron models. Furthermore, we show that a Spike-Timing Dependent Plasticity learning rule, shaped as a smoothed second derivative, implements SFA for spiking neurons. The theory is supported by numerical simulations, and to illustrate a simple use of SFA, we have applied it to auditory signals. We show that a single SFA neuron can learn to extract the tempo in sound recordings.
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Registered trials
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