Structural Levels of Organization in The Brain

The human nervous system may be viewed as a three-stage system, as depicted in the block diagram of figure Central to the system is the brain, represented by the neural (nerve) net in figure, which continually receives information, perceives it, and makes appropriate decisions. Two sets of arrows are shown in figure. Those pointing from left to right indicate the forward transmission of information bearing signals through the system. On the other hand, the arrows pointing from right to left signify the presence of feedback in the system. The receptor in figure convert stimuli from the human body or the external environment into electrical impulses that convey information to the neural net (brain). The effectors, on the other hand, convert electrical impulses generated by the neural net into discernible reponses as system outputs.

In the brain there are both small scale and large scale anatomical organization, and different functions take place at lower and higher levels. Proceeding upward from synapses that represent the most fundamental level and that depend on molecules and ions for their action, we have neural microcircuits, dendritic trees, and then neurons. A neural microcircuit refers to an assembly of syanpses organized into patterns of connectivity so as to produce a funtional operation of interest.

A neural microcircuit may be likened to a silicon chip made up of an assembly of transistors. The smallest size of microcircuits is measured in micrometers, and their fastest speed of operation is measured in milliseconds. The neural microcircuits are groupedto form dendritic subunits within the dendritic trees of inividual neurons. The whole neuron, about 100 micrometers in size, contains several dendritic subunits. At the next level of complexity, we have local circuits made up of nurons with similar of different properties, these neural assemblies perform operation characteristic of a localized region in the brain.

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What is A Neural Network

Work on artificial neural network, commonly referred to as neural networks, has been motivated right from its inception by the recognition that the brain computers in an entirely different way from the conventional digital computer. The struggle to understand the brain owes much to the pioneering work of Ramon, who introduced the idea of nerons as structural constituents of the brain. Typically, neurons are five to six orders of magnitude slower than silicon logic gates; events in a silicon chip happen in the nanosecond range, whereas neural events happen in the millisecond range.

The brain is a highly complex, nonlinear, and parallel computer (information processing system). It has the capability of organizing neurons so as to perform certain computations (eg. Pattern recognition, perception, and motor control) many times faster than the fastest digital computer in existence today). Consider for example human vision, which is an information processing task. It is the function of the visual system to provide a representation of the environment around us and, more important, to supply the information we need to interact with the environment. To be specific, the brain routinely accomplishes perceptual recognition tasks in something of the order of 100-200 ms, whereas tasks of much lesser complexity will take days on a huge conventional computer.

A neural network is a massively parallel distributed processor that has a natural propensity for storing experiental knowledge and making it available for use. It resembles the brain in two respects:
1. Knowledge is acquired by the network through a learning process
2. Interneuron connection strengths known as synaptic weights are used to store the knowledge.
Neural networks are also referred to in the literature as neurocomputers, connectionist networks, parallel distributed processors, etc. Throughout the book we use the term neural networks; occasionally, the term “neurocomputer” or “connectionist network” is used.

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