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In this paper, a framework based on algebraic structures to formalize various types of neural networks is presented. The working strategy is to break down neural networks into building blocks, relationships between each building block, and their operations. Building blocks are collections of primary components or neurons. In turn, neurons are collections of properties functioning as single entities, transforming an input into an output. We perceive a neuron as a function. Thus the flow ofdoi:10.2306/scienceasia1513-1874.2014.40.094 fatcat:drglcbz4y5h2bh2h4fqyl7uqki