Nature Neuroscience 9, 420 - 428 (2006)
Published online: 12 February 2006; Corrected online: 14 February 2006 | doi:10.1038/nn1643
The tempotron: a neuron that learns spike timing–based decisionsRobert Gütig1, 2, 3, 4
& Haim Sompolinsky1, 2, 51
Racah Institute of Physics, Hebrew University, 91904 Jerusalem, Israel. 2
Interdisciplinary Center for Neural Computation, Hebrew University, 91904 Jerusalem, Israel. 3
Institute for Theoretical Biology, Humboldt University, 10115 Berlin, Germany. 4
Neuroscience Research Center, Charitè Medical Faculty of Berlin, 10117 Berlin, Germany. 5
Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA.
Correspondence should be addressed to Haim Sompolinsky haim@fiz.huji.ac.il or Robert Gütig guetig@cc.huji.ac.il 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.Note: The PDF version of this article was corrected on the 14th of February, and the HTML version on the 16th of February. Please see the PDF for details.
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