Spiking Neural Networks and Synaptics
Mohamad Hassoun, author of Fundamentals of Artificial Neural Networks (MIT Press, 1995) and a professor of electrical and computer engineering at Wayne State University, adapts an introductory section from his book in response.
Artificial neural networks are parallel computational models, comprising densely interconnected adaptive processing units. These networks are composed of many but simple processors (relative, say, to a PC, which generally has a single, powerful processor) acting in parallel to model nonlinear static or dynamic systems, where a complex relationship exists between an input and its corresponding output.
A very important feature of these networks is their adaptive nature, in which "learning by example" replaces "programming" in solving problems. Here, "learning" refers to the automatic adjustment of the system's parameters so that the system can generate the correct output for a given input; this adaptation process is reminiscent of the way learning occurs in the brain via changes in the synaptic efficacies of neurons. This feature makes these models very appealing in application domains where one
Source: frontiers in neurosciences
Artificial Neural Network Training and Software Implementation Techniques (Computer Networks)
Book (Nova Science Publishers)
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