The tempotron: A neuron that learns spike timing-based decisions

Robert Gütig*, Haim Sompolinsky

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

659 Scopus citations

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.

Original languageEnglish
Pages (from-to)420-428
Number of pages9
JournalNature Neuroscience
Volume9
Issue number3
DOIs
StatePublished - Mar 2006

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