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 use a reservoir computing framework to explain how such neural sequences can be generated and employed in temporal tasks. We propose a general solution for recurrent neural 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 in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain.
Footnotes
P.V.L. conceived the model and ran the simulations. P.V.L. and M.C. performed the data analysis. P.V.L. and J.P.T. designed the experiments. P.V.L., M.C. and J.P.T. wrote the manuscript.
The authors declare that they have no conflict of interest. No research involving human participants or animals was performed.