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Learning spatiotemporal signals using a recurrent spiking network that discretizes time
2020
PLoS Computational Biology
Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network
doi:10.1371/journal.pcbi.1007606
pmid:31961853
fatcat:xlbuzh255nc2fp2bhfkuf4gdmq