Name
Abstract | ||
|---|---|---|
We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. The methods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1162/NECO_a_00156 | Neural Computation |
Keywords | Field | DocType |
expectation maximization,monte carlo,real time,nucleus,variability,systems,noise,point process,signal processing,state space model,monte carlo methods | Signal processing,Monte Carlo method,Monte carlo estimation,Inference,Computer science,Point process,Filter (signal processing),Artificial intelligence,State space,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
23 | 8 | 0899-7667 |
Citations | PageRank | References |
5 | 0.60 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Zammit Mangion | 1 | 11 | 2.65 |
| Ke Yuan | 2 | 29 | 3.80 |
| Visakan Kadirkamanathan | 3 | 431 | 62.00 |
| Mahesan Niranjan | 4 | 775 | 120.43 |
| Guido Sanguinetti | 5 | 772 | 57.09 |