Functional magnetic resonance imaging (fMRI) is the dominating approach to research in the mapping of neural activity in the human brain. State of the art data analysis techniques employ a statistical parametric mapping (SPM) strategy to convert raw signal into interpretable images by processing data in a pipeline of task-specific modules. This approach, despite its simplicity and reliability, presents a set of inconveniences, including low interconnectivity among modules, resulting in suboptimal solutions. In this project we aim at making a major contribution to the field by replacing the step-by-step data processing pipeline by a deep neural network. We hypothesise that this will achieve better solutions by propagating the effects of module-based decisions through the network, jointly optimizing the whole processing pipeline. Moreover, the proposed architecture can be used to analyse the resulting data processing pipelines, a tool that helps in better understand the influence that each module has to the final task being investigated. We release a freely accessible software tool, supplying an easy-to-use GPU- powered framework to perform automatic multivariate non-linear data analysis of neurological signals.