This article points out some very serious misconceptions about the brain in connectionism and artificial neural networks. Some of the connectionist ideas have been shown to have l...
This paper investigates the identification of nonlinear systems by utilizing soft-computing approaches. As the identification methods, Feedforward Neural Network architecture (FNN...
This paper presents a novel self-creating neural network scheme which employs two resource counters to record network learning activity. The proposed scheme not only achieves the b...
A backpropagation learning algorithm for feedforward neural networks with an adaptive learning rate is derived. The algorithm is based upon minimising the instantaneous output erro...
Linear projection equations arise in many optimization problems and have important applications in science and engineering. In this paper, we present a recurrent neural network fo...
This work describes a neural network model of the rat exploratory behavior in the elevated plus-maze, a test used to study anxiety. It involves three parameters: drive to explore;...
The visual cortex has a laminar organization whose circuits form functional columns in cortical maps. How this laminar architecture supports visual percepts is not well understood...
William D. Ross, Stephen Grossberg, Ennio Mingolla
The proper functioning of the nervous system depends critically on the intricate network of synaptic connections that are generated during the system development. During the netwo...
We present the theoretical results about the construction of confidence intervals for a nonlinear regression based on least squares estimation and using the linear Taylor expansio...
Support vector (SV) machines are useful tools to classify populations characterized by abrupt decreases in density functions. At least for one class of Gaussian data model the SV ...