  The photonuclear reactions which is induced by high-energetic photon are one
of the important type of reactions in the nuclear structure studies. In this
reaction, a target material is bombarded by photons with the energies in the
range of gamma-ray energy scale and the photons can statistically be absorbed
by a nucleus in the target material. In order to get rid of the excess energies
of the excited target nuclei, it can first emit protons, neutrons, alphas and
light particles according to the separation energy thresholds. After this
emitting process, generally an unstable nucleus can be formed. By the
investigation of this products forming after photonuclear reactions, nuclear
structure information can be obtained. In the present work, ({\gamma}, n)
photonuclear reaction cross-sections on stable p-shell nuclei have been
estimated by using neural network method. The main purpose of this study is to
find neural network structures that give the best estimations on the
cross-sections and to compare them with each other and available literature
data. According to the results, the method is convenient for this task.

Nuclear Theory;Nuclear Experiment;Data Analysis, Statistics and Probability