Simplicial complexes for analysis of neural data
Event details
| Date | 09.10.2015 |
| Hour | 14:00 › 15:00 |
| Speaker | Chad Giusti (Warren Center for Network and Data Sciences, University of Pennsylvania) |
| Location |
Campus Biotech, Building B1, 6th floor
|
| Category | Conferences - Seminars |
Graphs have proven to be an exceptional data structure through which to address a broad range problems in neuroscience. However, they are intrinsically limited to the study of dyadic relationships, as represented by an edge (or its absence) between two population elements. In the brain, it is often clear that fundamental functional units of interest involve large groups of basic units, which suggests that graph models are insufficient for their study. Simplicial complexes offer a natural way to address this concern, with the added benefit of providing a bridge for the application of powerful topological tools. A central difficulty in using simplicial methods, however, is the construction from observations of complexes whose topological or combinatorial structure tells us something useful about the underlying neural system. Here, we discuss a pair of complexes, the order complex and the coincidence complex, that have proven effective for understanding neural data across various modalities, along with a discussion of how to measure interesting structure.
Practical information
- Informed public
- Free
Organizer
- Kathryn Hess