Networks that learn the precise timing of event sequences

Journal of Computational Neuroscience
Alan Veliz-CubaZachary P Kilpatrick

Abstract

Neuronal circuits can learn and replay firing patterns evoked by sequences of sensory stimuli. After training, a brief cue can trigger a spatiotemporal pattern of neural activity similar to that evoked by a learned stimulus sequence. Network models show that such sequence learning can occur through the shaping of feedforward excitatory connectivity via long term plasticity. Previous models describe how event order can be learned, but they typically do not explain how precise timing can be recalled. We propose a mechanism for learning both the order and precise timing of event sequences. In our recurrent network model, long term plasticity leads to the learning of the sequence, while short term facilitation enables temporally precise replay of events. Learned synaptic weights between populations determine the time necessary for one population to activate another. Long term plasticity adjusts these weights so that the trained event times are matched during playback. While we chose short term facilitation as a time-tracking process, we also demonstrate that other mechanisms, such as spike rate adaptation, can fulfill this role. We also analyze the impact of trial-to-trial variability, showing how observational errors as well as ne...Continue Reading

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Citations

Apr 17, 2020·PloS One·Elif Köksal ErsözFrédéric Lavigne
Dec 3, 2016·Cerebral Cortex·Peter KaposvariRufin Vogels
Feb 6, 2020·Frontiers in Computational Neuroscience·Ulises Pereira, Nicolas Brunel
Nov 3, 2017·Molecular Informatics·Anvita GuptaGisbert Schneider
Nov 13, 2020·Proceedings of the National Academy of Sciences of the United States of America·Maxwell GillettNicolas Brunel
Jun 28, 2021·Current Opinion in Neurobiology·Johnatan AljadeffNicolas Brunel

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