Name
Title | ||
|---|---|---|
Analysis of Retinal Ganglion Cells Population Responses Using Information Theory and Artificial Neural Networks: Towards Functional Cell Identification |
Abstract | ||
|---|---|---|
In this paper, we analyse the retinal population data looking at behaviour. The method is based on creating population subsets using the autocorrelograms of the cells and grouping them according to a minimal Euclidian distance. These subpopulations share functional properties and may be used for data reduction, extracting the relevant information from the code. Information theory (IT) and artificial neural networks (ANNs) have been used to quantify the coding goodness of every subpopulation, showing a strong correlation between both methods. All cells that belonged to a certain subpopulation showed very small variances in the information they conveyed while these values were significantly different across subpopulations, suggesting that the functional separation worked around the capacity of each cell to code different stimuli. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02264-7_14 | IWINAC (1) |
Keywords | Field | DocType |
artificial neural networks,information theory,towards functional cell identification,certain subpopulation,functional separation,subpopulations share,data reduction,different stimulus,artificial neural network,functional property,retinal population data,relevant information,retinal ganglion cells population | Information theory,Population,Retinal ganglion,Spike train,Neural coding,Computer science,Retinal ganglion cell,Artificial intelligence,Mutual information,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
5601 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. P. Bonomini | 1 | 1 | 2.17 |
| J. M. Ferrández | 2 | 8 | 4.63 |
| J. Rueda | 3 | 0 | 0.34 |
| Eduardo B. Fernandez | 4 | 1653 | 429.84 |