A general purpose simulation environment for neural models

Current interest in neural networks has produced a diverse set of algorithms and architectures that vary in connectivity pattern, temporal behavior, update rules, and convergence properties. The authors have designed a flexible simulation system that can support the implementation of a wide range of neural network approaches. The UCLA-SFINX simulator is especially suited for the exploration of structured, irregular, and layered connectivity patterns. Functions, such as those in early vision, are modeled using the regular connectivity of center/surround antagonistic receptive fields and can be implemented as the difference of concentric gaussians. Higher level cognitive functions, such as supervised and unsupervised learning, have more irregular, dynamic connectivity structures and update mechanisms that are also supported. To visualize weight spaces, input/output training sets, image data, or other network characteristics, SFINX provides an X-windows based graphical output that assists in rapidly assessing the consequences of altering connectivity patterns, parameter tuning, and other experiments.