Countries are trying to equip their public transportation infrastructure with fixed radiation portals and detectors to detect radiological threat. Current works usually focus on neutron detection, which could be useless in the case of dirty bomb that would not use fissile material. Another approach, such as gamma dose rate variation monitoring is a good indication of the presence of radionuclide. However, some legitimate products emit large quantities of natural gamma rays; environment also emits gamma rays naturally. They can lead to false detections. Moreover, such radio-activity could be used to hide a threat such as material to make a dirty bomb. Consequently, radionuclide identification is a requirement and is traditionally performed by gamma spectrometry using unique spectral signature of each radionuclide. These approaches require high-resolution detectors, sufficient integration time to get enough statistics and large computing capacities for data analysis. High-resolution detectors are fragile and costly, making them bad candidates for large scale homeland security applications. Plastic scintillator and NaI detectors fit with such applications but their resolution makes identification difficult, especially radionuclides mixes. This paper proposes an original signal processing strategy based on artificial spiking neural networks to enable fast radionuclide identification at low count rate and for mixture. It presents results obtained for different challenging mixtures of radionuclides using a NaI scintillator. Results show that a correct identification is performed with less than hundred counts and no false identification is reported, enabling quick identification of a moving threat in a public transportation. Further work will focus on using plastic scintillators.