  We propose a novel method for identification of a linear pattern of pixels on
a two-dimensional grid. Following principles employed by the visual cortex, we
employ orientation selective neurons in a neural network which performs this
task. The method is then applied to a sample of data collected with the ZEUS
detector at HERA in order to identify cosmic muons which leave a linear pattern
of signals in the segmented uranium-scintillator calorimeter. A two dimensional
representation of the relevant part of the detector is used. The results
compared with a visual scan point to a very satisfactory cosmic muon
identification. The algorithm performs well in the presence of noise and pixels
with limited efficiency. Given its architecture, this system becomes a good
candidate for fast pattern recognition in parallel processing devices.
