Slow feature analysis with spiking neurons and its application to audio stimuli
- 395 Downloads
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.
Keywordsunsupervised learning plasticity slow feature analysis
Compliance with Ethical Standards
Conflict of interests
The authors declare that they have no conflict of interest.
- Dähne, S., Wilbert, N., & Wiskott, L. (2014). Slow Feature Analysis on retinal waves leads to V1 complex cells PLos computational biology 10(5):e1003564.Google Scholar
- Gibson, J.J. (1986). The Ecological Approach to Visual Perception.Google Scholar
- Goodman, D, & Brian, R.B. (2008). A simulator for spiking neural networks in python. Frontiers in Neuroinformatics, 2.Google Scholar
- Hebb, D.O.O. (1949). The organization of behavior: a neuropsychological theory. Science Education, 44, 335.Google Scholar
- Marr, D. (1970). A theory for cerebral neocortex. Proceedings of the Royal Society of London. Series B, 176, 161–234.Google Scholar
- Stevenson, I.H., Cronin, B., Sur, M., & Kording, K.P. (2010). Sensory adaptation and short term plasticity as bayesian correction for a changing brain, Vol. 5.Google Scholar
- Wiskott, L., & Berkes, P. (2003). Is slowness a learning principle of the visual cortex, (Vol. 106.Google Scholar