Scalable classification in large scale spatiotemporal domains applied to voltage-sensitive dye imaging

We present an approach for learning models that obtain accurate classification of large scale data objects, collected in spatiotemporal domains. The model generation is structured in three phases: pixel selection (spatial dimension reduction), spatiotemporal features extraction and feature selection. Novel techniques for the first two phases are presented, with two alternatives for the middle phase. Model generation based on the combinations of techniques from each phase is explored. The introduced methodology is applied on datasets from the Voltage-Sensitive Dye Imaging (VSDI) domain, where the generated classification models successfully decode neuronal population responses in the visual cortex of behaving animals. VSDI currently is the best technique enabling simultaneous high spatial (10, 000 points) and temporal (10 ms or less) resolution imaging from neuronal population in the cortex. We demonstrate that not only our approach is scalable enough to handle computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding abilities.