OVERVIEW OF NEURAL NETWORKS. This installment addresses the subject of computer-models of neural networks and the relevance of those models to the functioning brain. The computer field of Artificial Intelligence is a vast bottomless pit which would lead this series too far from biological reality -- and too far into speculation -- to be included. Neural network theory will be the singular exception because the model is so persuasive and so important that it cannot be ignored. Neurobiology provides a great deal of information about the physiology of individual neurons as well as about the function of nuclei and other gross neuroanatomical structures. But understanding the behavior of networks of neurons is exceedingly challenging for neurophysiology, given current methods.
Nonetheless, network behavior is important, especially in light of evidence for so-called "emergent properties", ie, properties of networks that are not obvious from an understanding of neuron physiology. Crash Introduction to Artificial Neural Networks. 1. Neurobiological Background This is a result worth of the Nobel Prize [1906]. The neuron is a many-inputs / one-output unit. The output can be excited or not excited , just two possible choices (like a flip-flop). The signals from other neurons are summed together and compared against a threshold to determine if the neuron shall excite ("fire"). The next important step was to find that the synapse resistance to the incoming signal can be changed during a "learning" process [1949]: This discovery became a basis for the concept of associative memory (see below). 2.
The Artificial Neuron is actually quite simple. 3. The power of neuron comes from its collective behavior in a network where all neurons are interconnected. One observation is that the evolving of ANN causes it to eventually reach a state where all neurons continue working but no further changes in their state happen. 3.1. 3.1.1. So, simply put, we created a thing which learns how to recognize certain input patterns. 3.1.2. 4. Introduction to Neural Networks Competitive Learning and Self Organized Maps.
Introduction to Neural Networks. Artificial Neural Networks course. Neural Networks. Neural Networks. Abstract This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated. Contents: 1. 1.1 What is a neural network? 1.2 Historical background 1.3 Why use neural networks? 1.4 Neural networks versus conventional computers - a comparison 2. 2.1 How the Human Brain Learns?
2.2 From Human Neurones to Artificial Neurones 3. 3.1 A simple neuron - description of a simple neuron 3.2 Firing rules - How neurones make decisions 3.3 Pattern recognition - an example 3.4 A more complicated neuron 4. 4.1 Feed-forward (associative) networks 4.2 Feedback (autoassociative) networks 4.3 Network layers 4.4 Perceptrons 5. 5.1 Transfer Function 5.3 The Back-Propagation Algorithm. Constructive Neural Network Learning Algorithms for Pattern Classification. Constructive Neural Network Learning Algorithms for Pattern Classification Personnel Project Summary Funding Publications Additional Information Projects AI Lab PersonnelDr. Vasant Honavar, Principal Investigator. Dr. Karthik Balakrishnan, Former Ph.D. Project Summary Induction of pattern classifiers from data is an important area of research in machine learning which finds applications in diverse areas including automated diagnosis, bioinformatics, design of customizable information assistants, intrusion detection in computer systems, among others.
Constructive learning algorithms, which avoid the guesswork involved in deciding a suitable network architectures for different pattern classification problems by growing a network by recruiting neurons as needed can be effectively trained to solve complex pattern classification problems. Funding Constructive Neural Network Learning Algorithms for Pattern Classification, National Science Foundation, (1994-1999). Publications To appear. Dr.