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• W1504988924 abstract "Our continuous perception of the world, our sensations about light, colour, music, speech, taste, smell is turned into and coded as binary data by the peripheral sensory systems, and sent by the corresponding nerves to the brain where this code is interpreted and coloured with emotions. The binary sensory data consists in sequences of identical voltage peaks, called action potentials or spikes. Seeing consists in decoding the patterns of these spike trains which are sent to the brain, via the optic nerve, by the visual transduction element: the retina. The external world object features, such as size, colour, intensity, are transformed by the retina in a myriad of parallel spikes sequences, which must describe with precision and robustness all the characteristics perceived. Getting insight into this population code is, nowadays, a basic question for visual science. A considerable number of coding studies have focused on single ganglion cell responses. Traditionally, the spiking rate of aisle cells has been used as an information carrier due to the close correlation with the stimulus intensity in all sensory systems. There are, however some drawbacks when analysing single cell firings. Firstly, the response of a single cell cannot unequivocally describe the stimulus since the response from a single cell to the same stimulus has a considerable variability for different presentations. Moreover, the timing sequence differs not only in the time events but also in the spike rates, producing uncertainty in the decoding process. Secondly, the same sequence of neuronal events in an aisle cell may be obtained by providing different stimuli, introducing ambiguity in the neuronal response. New recording techniques arisen from emerging technologies, allow simultaneous recordings from large populations of retinal ganglion cells. At this time, recordings in the order of a hundred simultaneous spike trains may be obtained. New tools for analysing this huge volume of data must be used and turn out to be critical for proper conclusions. FitzHugh used a statistical analyser which, applied to neural data was able to estimate stimulus features. Different approaches have been proposed on the construction of such a functional population-oriented analizer, including information theory, linear filters, discriminant analysis and neural networks. Analyzing the neural code, especially when this code is split by clustering algorithms in the search of certain levels of organization within it, implies to quantify the amount of" @default.
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• W1504988924 date "2011-04-11" @default.
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• W1504988924 title "Artificial Neural Networks and Retinal Ganglion Cell Responses" @default.
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• W1504988924 doi "https://doi.org/10.5772/15954" @default.
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