title: Slow feature analysis with spiking neurons and its application to audio stimuli.
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.
authors.affiliation: Institut de la Vision, Sorbonne Université, UPMC Univ Paris06 UMRS968, Paris, France. guillaume.bellec@ensta.org.|INSERM, U968, Paris, France. guillaume.bellec@ensta.org.|CNRS, UMR7210, Paris, France. guillaume.bellec@ensta.org.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Bellec
authors.affiliation: European Institute for Theoretical Neuroscience CNRS UNIC UPR-3293, Paris, France.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Galtier
authors.affiliation: Institut de la Vision, Sorbonne Université, UPMC Univ Paris06 UMRS968, Paris, France.|INSERM, U968, Paris, France.|CNRS, UMR7210, Paris, France.
authors.forename: Romain
authors.initials: R
authors.lastname: Brette
authors.affiliation: Institut de la Vision, Sorbonne Université, UPMC Univ Paris06 UMRS968, Paris, France.|INSERM, U968, Paris, France.|CNRS, UMR7210, Paris, France.|Institut d'Etudes de la Cognition, ENS, Paris, France.
authors.forename: Pierre
authors.initials: P
authors.lastname: Yger
mesh_terms: D000161:Acoustic Stimulation; D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001307:Auditory Perception / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*
keywords: plasticity; slow feature analysis; unsupervised learning