Networks that learn the precise timing of event sequences.
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 neuronal noise result in variability in learned event times. The dynamics of the playback process determines how stochasticity is inherited in learned sequence timings. Future experiments that characterize such variability can therefore shed light on the neural mechanisms of sequence learning.
Department of Mathematics, University of Houston, Houston, TX, 77204, USA. alanavc@math.uh.edu.
Alan
A
Veliz-Cuba
Department of Neurobiology and Anatomy, University of Texas Medical School, Houston, TX, 77030, USA. Harel.Shouval@uth.tmc.edu.
Harel Z
HZ
Shouval
Department of Mathematics, University of Houston, Houston, TX, 77204, USA. josic@math.uh.edu.|Department of Biology, University of Houston, Houston, TX, 77204, USA. josic@math.uh.edu.
Krešimir
K
Josić
Department of Mathematics, University of Houston, Houston, TX, 77204, USA. zpkilpat@math.uh.edu.
Zachary P
http://orcid.org/0000-0002-2835-9416
ZP
Kilpatrick
D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology; D000069550:Machine Learning; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity; D017966:Pyramidal Cells / Q000502:physiology; D013569:Synapses / Q000502:physiology; D013995:Time; D013998:Time Perception / Q000502:physiology
Long term plasticity; Serial recall; Short term facilitation