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.
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We thank L. de Hoz, A. Globerson, M. Gutnick, D. Hansel, O. White and Y. Yarom for comments. We acknowledge computational resources provided by the Harvard University Bauer Center for Genomic Research. This work was supported in part by the German Research Foundation, the Minerva Foundation, the European Commission's Improving Human Potential Program, the Israel Science Foundation (Center of Excellence no. 8006/00) and the Defense Research & Development Directorate (MAFAT).
The authors declare no competing financial interests.
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Gütig, R., Sompolinsky, H. The tempotron: a neuron that learns spike timing–based decisions. Nat Neurosci 9, 420–428 (2006). https://doi.org/10.1038/nn1643
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