Slow Ramping Emerges from Spontaneous Fluctuations in Spiking Neural Networks

Authors

Jake Gavenas, Chapman UniversityFollow
Ueli Rutishauer, Cedars-Sinai Medical Center
Aaron Schurger, Chapman UniversityFollow
Uri Maoz, Chapman UniversityFollow

Document Type

Article

Publication Date

8-24-2024

Abstract

The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.

Comments

This article was originally published in Nature Communications, volume 15, in 2024. https://doi.org/10.1038/s41467-024-51401-x

Recommended Citation

Gavenas, J., Rutishauser, U., Schurger, A. et al. Slow ramping emerges from spontaneous fluctuations in spiking neural networks. Nat Commun 15, 7285 (2024). https://doi.org/10.1038/s41467-024-51401-x

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The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.