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Learning Universal Computations with Spikes
2016
PLoS Computational Biology
Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g.~for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive
doi:10.1371/journal.pcbi.1004895
pmid:27309381
pmcid:PMC4911146
fatcat:oxs4rcxac5cwlarm5udw5iuqnu