Name
Title | ||
|---|---|---|
Using probabilistic dependencies improves the search of conductance-based compartmental neuron models |
Abstract | ||
|---|---|---|
Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that the interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12211-8_15 | EvoBIO |
Keywords | Field | DocType |
electrophysiological property,conductance-based compartmental neuron model,neuron model conductances,optimization problem,different estimation,probabilistic dependency,biological experimental data,distribution algorithm,eda behavior,optimization process,probabilistic model,estimation of distribution algorithm,distributed algorithm | EDAS,Biological neuron model,Estimation of distribution algorithm,Experimental data,Computer science,Artificial intelligence,Probabilistic logic,Conductance,Optimization problem,Machine learning | Conference |
Volume | ISSN | ISBN |
6023 | 0302-9743 | 3-642-12210-8 |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Santana | 1 | 357 | 19.04 |
| Concha Bielza | 2 | 909 | 72.11 |
| Pedro Larrañaga | 3 | 3882 | 208.54 |