Fluorescence molecular tomography (FMT) has gained prominence in recent years as a viable optical imaging technique for non-invasive, high-sensitivity, tomographic imaging of the brain. While optical imaging methods have demonstrated promising results for quantitative imaging of functional changes in the brain, they are still limited in their abilities to achieve high spatial and temporal resolution. To address these challenges, we present here a deep learning solution for FMT reconstructions, which implements a neural network with our novel asymptotic sparse function from our previously introduced sensitivity equation-based non-iterative sparse optical reconstruction (SENSOR) code to achieve high-resolution and sparse reconstructions using only learned parameters. We evaluate the proposed network through numerical phantom experiments. Furthermore, once the network is trained, the total reconstruction time is independent of the number of sources and wavelengths used.