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The tempotron: a neuron that learns spike timing-based decisions.
- Source :
-
Nature neuroscience [Nat Neurosci] 2006 Mar; Vol. 9 (3), pp. 420-8. Date of Electronic Publication: 2006 Feb 12. - Publication Year :
- 2006
-
Abstract
- The timing of action potentials in sensory neurons contains substantial information about the eliciting stimuli. Although the computational advantages of spike timing-based neuronal codes have long been recognized, it is unclear whether, and if so how, neurons can learn to read out such representations. We propose a new, biologically plausible supervised synaptic learning rule that enables neurons to efficiently learn a broad range of decision rules, even when information is embedded in the spatiotemporal structure of spike patterns rather than in mean firing rates. The number of categorizations of random spatiotemporal patterns that a neuron can implement is several times larger than the number of its synapses. The underlying nonlinear temporal computation allows neurons to access information beyond single-neuron statistics and to discriminate between inputs on the basis of multineuronal spike statistics. Our work demonstrates the high capacity of neural systems to learn to decode information embedded in distributed patterns of spike synchrony.
- Subjects :
- Algorithms
Animals
Humans
Nerve Net physiology
Neurons, Afferent classification
Reaction Time physiology
Signal Processing, Computer-Assisted
Synaptic Transmission physiology
Time Factors
Action Potentials physiology
Afferent Pathways physiology
Brain physiology
Learning physiology
Models, Neurological
Neurons, Afferent physiology
Subjects
Details
- Language :
- English
- ISSN :
- 1097-6256
- Volume :
- 9
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Nature neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 16474393
- Full Text :
- https://doi.org/10.1038/nn1643