We present a biologically inspired approach to traffic sign detection based on Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN). VG-RAM WNN are effective machine learning tools that offer simple implementation and fast training and test. Our VG-RAM WNN architecture models the saccadic eye movement system and the transformations suffered by the images captured by the eyes from the retina to the superior colliculus in the mammalian brain. We evaluated the performance of our VG-RAM WNN system on traffic sign detection using the German Traffic Sign Detection Benchmark (GTSDB). Using only 12 traffic sign images for training, our system was ranked between the first 16 methods for the prohibitory category in the German Traffic Sign Detection Competition, part of the IJCNN'2013. Our experimental results showed that our approach is capable of reliably and efficiently detect a large variety of traffic sign categories using a few training samples.