A Neural Network Approach for the Analysis of Multineural Recordings in Retinal Ganglion Cells

In this paper the coding capabilities of individual retinal ganglion cells are compared with respect to the coding capabilities of small population of cells using different neural networks. This approach allows not only the identification of the most discriminating cells, but also detection of the parameters that are more important for the discrimination task. Our results show that the spike rate together with the exact timing of the first spike at light-ON were the most important parameters for encoding stimulus features. Furthermore we found that whereas single ganglion cells are poor classifiers of visual stimuli, a population of only 15 cells can distinguish stimulus color and intensity reasonable well. This demonstrates that visual information is coded as the overall set of activity levels across neurons rather than by single cells.