Networks that learn the precise timing of event sequences

TL;DR

This paper introduces a neural network model that learns both the order and precise timing of event sequences, combining long-term plasticity with short-term facilitation or other mechanisms for accurate replay.

Contribution

It presents a novel model integrating long-term plasticity and short-term facilitation to learn and reproduce exact timing in neural sequences.

Findings

Sequence learning involves shaping excitatory connectivity via plasticity.

Short-term facilitation enables temporally precise replay.

Variability in learned timings arises from neuronal noise and observational errors.

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 neuronal noise result in variability in learned event times. The dynamics of the playback process determine 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.