Name
Abstract | ||
|---|---|---|
In this paper, we consider a neuronal ensemble under spontaneous activity where each neuron modulates the activity of the others through its spiking history. Assuming that the cross-history dependence parameters of the ensemble are sparse and time-varying, we perform adaptive system identification using sparse point process filters. We then provide a novel filtering and smoothing algorithm for estimating the Granger causality with high temporal resolution and with recursively computed statistical confidence intervals. We provide simulation studies which reveal significant performance gains obtained by our proposed technique in describing the causal influences in neuronal ensemble activity. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CISS.2016.7460562 | 2016 Annual Conference on Information Science and Systems (CISS) |
Keywords | Field | DocType |
Granger causality,filtering and smoothing,noncentral chi-squared distribution,deviance statistics,sparsity | Time series,Computer science,Granger causality,Statistical Confidence,Point process,Artificial intelligence,Temporal resolution,Recursion,Mathematical optimization,Filter (signal processing),Algorithm,Smoothing,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| alireza sheikhattar | 1 | 7 | 2.16 |
| Babadi, Behtash | 2 | 325 | 36.16 |