Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations.

ABSTRACT: Many cognitive and behavioral tasks-such as interval timing, spatial navigation, motor control, and speech-require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control, and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found i

SUBMITTER: Vincent-Lamarre P 

PROVIDER: S-EPMC7505196 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature