Analysis of complex neural circuits with nonlinear multidimensional hidden state models
Abstract
In analyzing complex networks, we are commonly interested in quantifying the influence that the network nodes exert on each other and in decoding the behavior of the network. We present the nonlinear multidimensional hidden state (NMHS) model, which addresses both of these unmet challenges by simultaneously decoding activity from parallel data streams and calculating the interaction strength among them. In NMHS models, each node in a network acts as a stochastic process that can influence the progression of other nodes in the network. We show that our procedure matches or outperforms state-of-the-art techniques in a multitude of scenarios, notably in systems with nonlinear interactions.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 24, 2016
- Source ID
- 10.1073/pnas.1606280113
Entities
People
- Alanna F. Slocum
- Alex Altshuler
- Alexander Friedman
- Ann Graybiel
- Danil Tyulmankov
- Dirk W. Beck
- Jacquelyn E. C. Sholes
- Leif G. Gibb
- Qinru Shi
- Sebastian E. Toro Arana
- Suthee Ruangwises
Organizations
- Bar-Ilan University
- Boston University
- Harvard University
- Institute for National Security Studies
- Massachusetts Institute of Technology
- National Institute of Mental Health