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Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as medicine, engineering, geology, finance and physics. Indeed, anywhere that there are problems of prediction, classification or control, neural networks are being introduced.
In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. These connections are called synapses, are usually formed from axons to dendrites, though dendrodendritic microcircuits and other connections are possible. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex (Arbib 2002). Now a day the term neural network often refers to artificial neural networks, which are composed of artificial neurons or nodes.
Biological neural networks which are made up of real biological neurons. These Biological neural networks are connected or functionally related in the peripheral nervous system or the central nervous system. They are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
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