ARTIFICIAL NEURAL NETWORKS

 INTRODUCTION

Artificial neural networks (ANNs) provide a general, practical method for learning real-valued, discrete-valued, and vector-valued target functions.

Biological Motivation

• The study of artificial neural networks (ANNs) has been inspired by the observation that biological learning systems are built of very complex webs of interconnected Neurons.
• Human information processing system consists of brain neuron: basic building block cell that communicates information to and from various parts of body.

 Facts of Human Neurobiology

• Number of neurons ~ 10¹¹
• Connection per neuron ~ 10 ^{4 – 5}
• Neuron switching time ~ 0.001 second or 10 ^-3
• Scene recognition time ~ 0.1 second
• 100 inference steps doesn’t seem like enough
• Highly parallel computation based on distributed representation

 Properties of Neural Networks

• Many neuron-like threshold switching units
• Many weighted interconnections among units
• Highly parallel, distributed process
• Emphasis on tuning weights automatically
• Input is a high-dimensional discrete or real-valued (e.g, sensor input )