Population coding in spike trains of simultaneously recorded retinal ganglion cells

To achieve a better understanding of the parallel information processing that takes place in the nervous system, many researchers have recently begun to use multielectrode techniques to obtain high spatial- and temporal-resolution recordings of the firing patterns of neural ensembles. Apart from the complexities of acquiring and storing single unit responses from large numbers of neurons, the multielectrode technique has provided new challenges in the analysis of the responses from many simultaneously recorded neurons. This paper provides insights into the problem of coding/decoding of retinal images by ensembles of retinal ganglion cells. We have simultaneously recorded the responses of 15 ganglion cells to visual stimuli of various intensities and wavelengths and analyzed the data using discriminant analysis. Models of stimulus encoding were generated and discriminant analysis used to estimate the wavelength and intensity of the stimuli. We find that the ganglion cells we have recorded from are non-redundant encoders of these stimulus features. While single ganglion cells are poor classifiers of the stimulus parameters, examination of the responses of only a few ganglion cells greatly enhances our ability to specify the stimulus wavelength and intensity. Of the parameters studied, we find that the rate of firing of the ganglion cells provides the most information about these stimulus parameters, while the timing of the first action potential provides almost as much information. While we are not suggesting that the brain is using these variables, our results show how a population of sensory neurons can encode stimulus features and suggest that the brain could potentially deduce reliable information about stimulus features from response patterns of retinal ganglion cell populations.