Thermal infrared imaging is an effective modality for developing robust methods of small target detection at large distances. However, low target contrast and high background clutter are two main challenges that limit the detection performance. We present bio-inspired spatio-temporal pre-processing of infrared video frames to deal with such challenges. The neurons in the early vision system of small flying insects have remarkable capability for noise filtering, contrast enhancement, signal compression and clutter suppression. These neurons were computationally modeled previously in two stages using a combination of linear and non-linear processing layers. The first stage models the adaptive temporal filtering mechanisms of insect photoreceptor cells. It improves the signal-to-noise-ratio, enhances target background discrimination and expands the possible range of signal variability. The second stage models the spatio-temporal adaptive filtering in the large monopolar cells that remove redundancy and increase target contrast. To show the performance gain achieved by such bio-inspired preprocessing, we perform small target detection experiments on real world high bit-depth infrared video sequences. Results show that the early biological vision based pre-processing significantly improves the performance of four standard infrared small moving target detection techniques. Specifically, the spatio-temporal preprocessing increase the detection rate (at 10-5 false alarm rate) of the best performing method by 100% and by up to 630% for the other methods. Our results are indicative of the strong potential of the bio-processing for allowing systems to detect smaller targets at longer distances in more cluttered environments.