An analog neuromime suitable for VLSI implementation

The continuous-time mathematics of a binary-decision making neuromime are presented. The model, generally describable as a simplification of the Hodgkin-Huxley model, seeks to mimic many properties of biological neurons including spatial and temporal summation of excitatory and inhibitory inputs, action potential evolution controlled by countervailing charge movements, and postinhibitory rebound and accommodation. A nonlinear, deterministic, state-variable model is developed and then extended into a non-linear stochastic model which seeks to capture the stochastic properties of natural neurons. Finally, a linearized state-variable model is developed for the statistical evolution of single-units; that analytic model should prove useful for studying interconnected networks of stochastic neuromimes

Neural nets : classical results and current problems

This note briefly discusses some of the classical results of McCulloch and Pitts. It then deals with some current research in neural nets. Several questions about neural nets are shown to be computationally difficult by showing that they are NP-Complete or worse. The size of neural nets necessary to compute functions or simulate machines is discussed, and some worst case optimal results are given. The statistics of randomly chosen autonomous nets are discussed, together with some approaches to understanding these statistics. Recent results on the behavior of analog nets are discussed, and the possibility of silicon implementation is mentioned

A Mixed-Signal Feed-Forward Neural Network Architecture Using A High-Resolution Multiplying D/A Conversion Method

Artificial Neural Networks (ANNs) are parallel processors capable of learning from a set of sample data using a specific learning rule. Such systems are commonly used in applications where human brain may surpass conventional computers such as image processing, speech/character recognition, intelligent control and robotics to name a few. In this thesis, a mixed-signal neural network architecture is proposed employs a high resolution Multiplying Digital to Analog Converter (MDAC) designed using Delta Sigma Modulation (DSM). To reduce chip are, multiplexing is used in addition to analog implementation of arithmetic operations. This work employs a new method for filtering the high bit-rate signals using neurons nonlinear transfer function already existing in the network. Therefore, a configuration of a few MOS transistors are replacing the large resistors required to implement the low-pass filter in the network. This configuration noticeably decreases the chip area and also makes multiplexing feasible for hardware implementation

Dynamics of neural nets

In this short paper, I plan to review the work I have done on neural nets over the course of the last 20 years. As one might reasonably expect the questions being asked and the approaches to solving them have evolved, but there are still fundamental questions which remain unanswered and are perhaps unanswerable with present techniques. I have used the word "dynamics" in the title to indicate that the major emphasis of the paper will be how the state of a neural net changes in the course of time either autonomously, that is without input, or in response to input. The problems of learning in neural nets will not be directly addressed, although I will argue that many questions about learning can be recast as questions about dynamics

Contributions to theory and algorithms of independent component analysis and signal separation

This thesis addresses the problem of blind signal separation (BSS) using independent component analysis (ICA). In blind signal separation, signals from multiple sources arrive simultaneously at a sensor array, so that each sensor array output contains a mixture of source signals. Sets of sensor outputs are processed to recover the source signals or to identify the mixing system. The term blind refers to the fact that no explicit knowledge of source signals or mixing system is available. Independent component analysis approach uses statistical independence of the source signals to solve the blind signal separation problems. Application domains for the material presented in this thesis include communications, biomedical, audio, image, and sensor array signal processing. In this thesis reliable algorithms for ICA-based blind source separation are developed. In blind source separation problem the goal is to recover all original source signals using the observed mixtures only. The objective is to develop algorithms that are either adaptive to unknown source distributions or do not need to utilize the source distribution information at all. Two parametric methods that can adapt to a wide class of source distributions including skewed distributions are proposed. Another nonparametric technique with desirable large sample properties is also proposed. It is based on characteristic functions and thereby avoids the need to model the source distributions. Experimental results showing reliable performance are given on all of the presented methods. In this thesis theoretical conditions under which instantaneous ICA-based blind signal processing problems can be solved are established. These results extend the celebrated results by Comon of the traditional linear real-valued model. The results are further extended to complex-valued signals and to nonlinear mixing systems. Conditions for identification, uniqueness, and separation are established both for real and complex-valued linear models, and for a proposed class of non-linear mixing systems.reviewe