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Biological Neural Networks

Artificial NN draw much of their inspiration from the biological nervous system. It is therefore very useful to have some knowledge of the way this system is organized.

Most living creatures, which have the ability to adapt to a changing environment, need a controlling unit which is able to learn. Higher developed animals and humans use very complex networks of highly specialized neurons to perform this task.

The control unit - or brain - can be divided in different anatomic and functional sub-units, each having certain tasks like vision, hearing, motor and sensor control. The brain is connected by nerves to the sensors and actors in the rest of the body.

The brain consists of a very large number of neurons, about 1011 in average. These can be seen as the basic building bricks for the central nervous system (CNS). The neurons are interconnected at points called synapses. The complexity of the brain is due to the massive number of highly interconnected simple units working in parallel, with an individual neuron receiving input from up to 10000 others.

The neuron contains all structures of an animal cell. The complexity of the structure and of the processes in a simple cell is enormous. Even the most sophisticated neuron models in artificial neural networks seem comparatively toy-like.

Structurally the neuron can be divided in three major parts: the cell body (soma), the dentrites, and the axon, see Figure 1.1 for an illustration.

Figure 1.1:  Simplified Biological Neurons.

The cell body contains the organelles of the neuron and also the `dentrites' are originating there. These are thin and widely branching fibers, reaching out in different directions to make connections to a larger number of cells within the cluster.

Input connection are made from the axons of other cells to the dentrites or directly to the body of the cell. These are known as axondentrititic and axonsomatic synapses.

There is only one axon per neuron. It is a single and long fiber, which transports the output signal of the cell as electrical impulses (action potential) along its length. The end of the axon may divide in many branches, which are then connected to other cells. The branches have the function to fan out the signal to many other inputs.

There are many different types of neuron cells found in the nervous system. The differences are due to their location and function.

The neurons perform basically the following function: all the inputs to the cell, which may vary by the strength of the connection or the frequency of the incoming signal, are summed up. The input sum is processed by a threshold function and produces an output signal. The processing time of about 1ms per cycle and transmission speed of the neurons of about 0.6 to 120 {ms} are comparingly slow to a modern computer [zell94, p24,] , [barr88, p35,].

The brain works in both a parallel and serial way. The parallel and serial nature of the brain is readily apparent from the physical anatomy of the nervous system. That there is serial and parallel processing involved can be easily seen from the time needed to perform tasks. For example a human can recognize the picture of another person in about 100 ms. Given the processing time of 1 ms for an individual neuron this implies that a certain number of neurons, but less than 100, are involved in serial; whereas the complexity of the task is evidence for a parallel processing, because a difficult recognition task can not be performed by such a small number of neurons, example taken from [zell94, p24,]. This phenomenon is known as the 100-step-rule.

Biological neural systems usually have a very high fault tolerance. Experiments with people with brain injuries have shown that damage of neurons up to a certain level does not necessarily influence the performance of the system, though tasks such as writing or speaking may have to be learned again. This can be regarded as re-training the network.

In the following work no particular brain part or function will be modeled. Rather the fundamental brain characteristics of parallelism and fault tolerance will be applied.

next up previous contents
Next: Definitions Up: Introduction Previous: The Project

Albrecht Schmidt
Mit Okt 4 16:45:34 CEST 2000