Neurocomputing: Picking the human brain

Neurocomputing : Picking the Human Brain

2006

INTRODUCTION

The concept of intelligence is complex , and hence many theories definitions and taxonomies have emerged to explain its essence . The limited triumph has given rise to the idea that such a multidimensional concept cannot be explained by a single theory . As an outcome , a multidisciplinary approach has led to significant advancement in the theory of intelligence . Consequently , the necessity to build intelligent systems has resulted in the development of a range of techniques . Over recent years , numerous computational intelligence paradigms have been

established . As an alternative form of information processing neurocomputing is fast becoming a recognized discipline , and several neural networks are already on the market (Kandel , 1979 . Neural networks are good at some things that usual computers are bad at . They do well , for example , at solving complex pattern-recognition problems implicit in understanding continuous speech , identifying handwritten characters , and determining that a target seen from various angles is in fact one and the same object . Neural networks parallel-process huge quantities of information . Yet for a long time the only way to implement them was by simulating them laboriously , inefficiently , and at huge expense on standard , serial computers . That circumstance is shifting Neurocomputers - hardware on which neural networks can be implemented efficiently - have reached the prototype stage at numerous companies and a few are already commercially available . All are coprocessor boards that plug into conventional machines . Developers include IBM Corp Science Applications International Corp (SAIC , Texas Instruments Corp , Hecht-Nielsen Neurocomputer Corp (HNC , and TRW Inc . For the meantime , researchers at Boston University , the Helsinki University of Technology , Johns Hopkins University , the University of California at San Diego , the California Institute of Technology , and other universities have been investigating the theory behind neural networks and exploring their potential to solve problems that have stumped algorithmic computing for decades

GENERAL DISCUSSION

Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons (Dmitry O . Gorodnichy , W . W . Armstrong . These methods derive their power from the collective processing of artificial neurons , the chief advantage being that such systems can learn and adapt to a changing environment . In knowledge-based neurocomputing , the emphasis is on the use and representation of knowledge about an application Explicit modeling of the knowledge represented by such a system remains a major research . The reason is that humans find it complicated to interpret the numeric representation of a neural network

The anatomical structure of a typical neuron is shown in Figure 1 . The diagram depicts the three key parts of a neuron Figure 1 . Anatomical structure of a typical neuron

er of 10-20 ?m . The dendrites extend the cell body and provide the key physical surface on which the neuron receives signals from other neurons . In various types of neurons , the length of the dendrites can vary from tens of microns to a few millimeters (Eliashberg , 1988 . The axon provides the pathway throughout which the neuron sends signals to other neurons...