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.< >