Abstract | ||
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High-dimensional neural recordings across multiple brain regions can be used to establish functional connectivity with good spatial and temporal resolution. We designed and implemented a novel method, Latent Dynamic Factor Analysis of High-dimensional time series (LDFA-H), which combines (a) a new approach to estimating the covariance structure among high-dimensional time series (for the observed variables) and (b) a new extension of probabilistic CCA to dynamic time series (for the latent variables). Our interest is in the cross-correlations among the latent variables which, in neural recordings, may capture the flow of information from one brain region to another. Simulations show that LDFA-H outperforms existing methods in the sense that it captures target factors even when within-region correlation due to noise dominates cross-region correlation. We applied our method to local field potential (LFP) recordings from 192 electrodes in Prefrontal Cortex (PFC) and visual area V4 during a memory-guided saccade task. The results capture time-varying lead-lag dependencies between PFC and V4, and display the associated spatial distribution of the signals. |
Year | Venue | DocType |
---|---|---|
2020 | NIPS 2020 | Conference |
Volume | ISSN | Citations |
33 | 1049-5258 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Heejong Bong | 1 | 0 | 0.34 |
Zongge Liu | 2 | 2 | 1.39 |
Ren Zhao | 3 | 48 | 15.88 |
Matthew A. Smith | 4 | 26 | 5.09 |
Valérie Ventura | 5 | 253 | 36.45 |
Robert E. Kass | 6 | 8 | 1.83 |