Emergence of orientation selective simple cells simulated in deterministic and stochastic neural networks
The processing of visual data in area 17 of the mammalian cortex is mainly performed by cells with receptive fields which are tuned to different orientations of input stimuli. The mechanisms underlying the emergence of receptive field properties of orientation selective cells are not well understood up to now. Recently, some models for the prenatal development of the receptive fields of orientation selective simple cells have been proposed, which emerge in neural networks trained by Hebb type unsupervised learning rules. These models, however, use different network architectures and are restricted to the case of identical input neurons. In this work, a biologically motivated neural network model with a general architecture is presented. It is trained with a Hebb type updating rule and with uncorrelated input. The input neurons are identified with retinal ganglion cells and exhibit mature Mexican hat type receptive fields. If the receptive fields of the input neurons have identical properties (deterministic model), a set of parameter domains is found, which characterize different kinds of receptive field maturation behaviour of the network. Results obtained by other authors with similar models are contained in this description as special cases. In addition, the more general and rarely investigated stochastic model, where random variations of the parameters describing the receptive fields of the input neurons occur, is investigated. A high sensitivity of the network against these random variations is obtained. In case of large variations of receptive field parameters of the ganglion cells, a qualitatively new kind of maturation behaviour appears. A significant part of the synaptic connections from ganglion cells to the cortical cell is removed and small simple cell receptive fields with only few lobes emerge. The stochastic model is found to provide a better description of the size, scatter and structure of receptive fields present in biological systems, than the deterministic model.