Network structure of cascading neural systems predicts stimulus propagation and recovery
Abstract
Objective. Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network’s local and global connectivity to these patterns and information processing remains largely unknown. Approach. Here, we demonstrate how network structure supports information processing through network dynamics in empirical and simulated spiking neurons using mathematical tools from linear systems theory, network control theory, and information theory. Main results. In particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks. Significance. Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 01, 2020
- Source ID
- 10.1088/1741-2552/abbff1
Entities
People
- Danielle Bassett
- Harang Ju
- Jason Z Kim
- John M Beggs
Organizations
- Alfred P. Sloan Foundation
- Army Research Office
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Institute for Scientific Interchange
- John D. and Catherine T. MacArthur Foundation
- National Institute of Mental Health
- National Institute of Neurological Disorders and Stroke
- National Institutes of Health
- National Science Foundation
- Office of Naval Research
- United States Army Research Laboratory