Name
Abstract | ||
|---|---|---|
We employ optimal control theory to design an event-based, minimum energy, desynchronizing control stimulus for a network of pathologically synchronized, heterogeneously coupled neurons. This works by optimally driving the neurons to their phaseless sets, switching the control off, and letting the phases of the neurons randomize under intrinsic background noise. An event-based minimum energy input may be clinically desirable for deep brain stimulation treatment of neurological diseases, like Parkinson's disease. The event-based nature of the input results in its administration only when it is necessary, which, in general, amounts to fewer applications, and hence, less charge transfer to and from the tissue. The minimum energy nature of the input may also help prolong battery life for implanted stimulus generators. For the example considered, it is shown that the proposed control causes a considerable amount of randomization in the timing of each neuron's next spike, leading to desynchronization for the network. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/s10827-012-0419-3 | Journal of Computational Neuroscience |
Keywords | Field | DocType |
Optimal control,Hodgkin-Huxley neuron,Hamilton-Jacobi-Bellman PDE,Desynchronization | Deep brain stimulation,Hodgkin huxley neuron,Background noise,Optimal control,Control theory,Stimulus (physiology),Neuron,Mathematics | Journal |
Volume | Issue | ISSN |
34 | 2 | 1573-6873 |
Citations | PageRank | References |
14 | 1.23 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Nabi | 1 | 14 | 1.23 |
| Mohammad Mirzadeh | 2 | 26 | 2.22 |
| Frederic Gibou | 3 | 16 | 1.61 |
| Jeff Moehlis | 4 | 276 | 34.17 |