A tool for simulating neural models

SFINX, a simulation environment for modeling a wide spectrum of neural architectures with both regular and irregular connectivity patterns, is discussed. SFINX is not based on a specific neural paradigm; it supports a variety of computational models ranging from simple convolution filters such as difference-of-Gaussians receptive fields used in image processing to learning paradigms such as backward error propagation. SFINX´s main components and data are described, and the representation of explicit and implicit networks is examined. An explicit network is a collection of data structures that contain information about each node in the network. The name implicit networks reflects the fact that a node´s activation value, output value, etc., are all distributed across a set of buffer array data structures and that the connectivity information is stored inside the node function. A simulation example is given to illustrate the use of SFINX