title: Analysis of neuronal networks in the visual system of the cat using statistical signals--simple and complex cells. Part II.
abstract: Superimposing additively a two-dimensional noise process to deterministic input signals (bars) the neurons of area 17 show a class-specific reaction for the task of signal extraction. Moving both parts of the signals simultaneously and varying the signal to noise ratio (S/N) the simple cells achieve the same performance as resulted from the psychophysical experiment. Type I complex cells extract moving deterministic signals (i.e. bars) from the stationary noise, whereas in the answers of Type II complex cells the statistical parts of the signals predominate. Considering the different cell types each as a series of a linear and a nonlinear system one obtains the cell specific space-time frequency and the amplitude characteristics.
mesh_terms: D000818:Animals; D002415:Cats; D002540:Cerebral Cortex / Q000502:physiology*; D004594:Electrophysiology; D005556:Form Perception / Q000502:physiology*; D008433:Mathematics; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D013223:Statistics as Topic; D014795:Visual Pathways / Q000502:physiology*