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2.2 Biological Neural Networks

The neural system of the human body consists of three stages: receptors, a neural network, and effectors. The receptors receive the stimuli either internally or from the external world, then pass the information into the neurons in a form of electrical impulses. The neural network then processes the inputs then makes proper decision of outputs. Finally, the effectors translate electrical impulses from the neural network into responses to the outside environment. Figure 2.1 shows the bidirectional communication between stages for feedback [Arb87].

Figure 2.1: Three Stages of Biological Neural System
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\centerline {\epsfysize=0.7in \epsfbox{./figures/fig3Stages.epsi}}\end{figure}

The fundamental element of the neural network is called a neuron. As shown in figure 2.2, a neuron mainly consists of three parts: dendrites, soma, and axon. Dentrites are the tree-like structure that receives the signal from surrounding neurons, where each line is connected to one neuron. Axon is a thin cylinder that transmits the signal from one neuron to others. At the end of axon, the contact to the dendrites is made through a synapse. The inter-neuronal signal at the synapse is usually chemical diffusion but sometimes electrical impulses. A neuron fires an electrical impulse only if certain condition is met [Zur92].

Figure 2.2: A Biological Neuron
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\centerline {\epsfysize=2.5in \epsfbox{./figures/figBioNeuron.epsi}}\end{figure}

The incoming impulse signal from each synapse to the neuron is either excitatory or inhibitory, which means helping or hindering firing. The condition of causing firing is that the excitatory signal should exceed the inhibitory signal by a certain amount in a short period of time, called the period of latent summation. As we assign a weight to each incoming impulse signal, the excitatory signal has positive weight and the inhibitory signal has negative weight. This way, we can say, ``A neuron fires only if the total weight of the synapses that receive impulses in the period of latent summation exceeds the threshold." [Arb87].


next up previous
Next: 2.3 Artificial Neural Networks Up: 2. Artificial Neural Networks Previous: 2.1 Background
Kiyoshi Kawaguchi
2000-06-17