Abstract
Artificial neural networks have been introduced as a novel computing paradigm (Kohenen 1988). Processing (or retrieving) in neural networks requires a collective interaction of a number of neurons. Output of neurons are computed based on the inputs from other neurons, weights associated with such inputs, and a non-linear activation function. Specifically, most artificial neurons follow a mathematical model that is expressed as:
where Wij is the weight, Yj(t) is the neuron input, N is the number of neurons connected to neuron 1, and F is a non linear function which is usually a sigmoid (Hopfield 1984) as shown below.
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Ryan, T.F., Delgado-Frias, J.G., Vassiliadis, S., Pechanek, G.G., Green, D.M. (1994). A Dataflow Approach for Neural Networks. In: Delgado-Frias, J.G., Moore, W.R. (eds) VLSI for Neural Networks and Artificial Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1331-9_15
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DOI: https://doi.org/10.1007/978-1-4899-1331-9_15
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