Name
Abstract | ||
|---|---|---|
Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neuroimage.2012.10.039 | NeuroImage |
Keywords | DocType | Volume |
Neural field model,Multi-resolution approximation (MRA),Expectation maximization (EM) algorithm | Journal | 66 |
ISSN | Citations | PageRank |
1053-8119 | 4 | 0.43 |
References | Authors | |
22 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P Aram | 1 | 31 | 3.87 |
| D R Freestone | 2 | 76 | 9.31 |
| M. Dewar | 3 | 64 | 3.76 |
| Kenneth Scerri | 4 | 42 | 4.36 |
| Viktor K. Jirsa | 5 | 537 | 44.52 |
| David B. Grayden | 6 | 254 | 29.89 |
| Visakan Kadirkamanathan | 7 | 431 | 62.00 |