Conference proceeding
Functional Reconstruction of Dyadic and Triadic Subgraphs in Spiking Neural Networks
COMPLEX NETWORKS & THEIR APPLICATIONS V, v 693, pp 697-708
01 Jan 2017
Abstract
Neural networks reconstructed from measurement data are known to exhibit various forms of nonrandom structures, including subgraph motifs and small-worldedness. It has been suggested such nonrandom structures are critical for neural information-processing; however, it is unclear how the topological structure of anatomical networks influences the reconstruction of functional networks. To better understand the importance of such nonrandom structures, we study how dyadic and triadic subgraphs are preserved during the reconstruction. We use a model-free information-theoretic measure, transfer entropy, to quantify the directional associations of pairwise neuronal activity. We employ multiplex networks to compare how dyadic and triadic subgraphs differ from structural to functional networks, with particular attention to recurrent connections. We find that certain structural subgraphs have more influence on the topology of the functional network than others.
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Details
- Title
- Functional Reconstruction of Dyadic and Triadic Subgraphs in Spiking Neural Networks
- Creators
- Myles Akin - Drexel UniversityAlex Onderdonk - Drexel UniversityRhonda Dzakpasu - Georgetown UniversityYixin Guo - Drexel University
- Contributors
- H Cherifi (Editor)S Gaito (Editor)W Quattrociocchi (Editor)A Sala (Editor)
- Publication Details
- COMPLEX NETWORKS & THEIR APPLICATIONS V, v 693, pp 697-708
- Series
- Studies in Computational Intelligence
- Publisher
- Springer Nature
- Number of pages
- 12
- Grant note
- DMS-1226180 / NSF; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000413483400055
- Scopus ID
- 2-s2.0-85007400796
- Other Identifier
- 991019167587304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Information Science & Library Science
- Social Sciences, Mathematical Methods