| Paper: | PS-1A.29 | ||
| Session: | Poster Session 1A | ||
| Location: | H Lichthof | ||
| Session Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Network structure of neural systems supporting cascading dynamics predicts stimulus propagation and recovery | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1247-0 | ||
| Authors: | Harang Ju, Jason Kim, Danielle Bassett, University of Pennsylvania, United States | ||
| Abstract: | Many neural systems display cascading behavior characterized by uninterrupted sequences of neuronal firing. When the distributions of cascade size and duration follow a power law, theoretical models suggest that such dynamics support optimal information transmission and storage. However, the unknown role of network structure on neural dynamics precludes an understanding of how variations in network structure either support or impinge upon information processing. Here, we develop a theoretical understanding of how network structure supports information processing through network dynamics and validate our theory with empirical data. Using a generalized spiking model and mathematical intuitions from linear systems theory, network control theory, and information theory, we show how network structure can be designed to temporally extend the propagation and recovery of certain stimulus patterns. Moreover, we observe cycles as structural and dynamic motifs that are prevalent in such networks. Broadly, our results demonstrate how network structure constrains cascading dynamics and supports persistent activation that could potentially contribute to cognitive faculties, such as working memory or attention. | ||