Biological computation networks

An artificial neural network is a parallel computational model comprised of densely interconnected adaptive processing elements called neurons or units. It is an information-processing system that has certain performance characteristics in common with the biological neural networks (Rumelhart McClelland, 1986). It resembles the... 

Surprisingly, despite the widespread use of Markov chains in many areas of science and technology such as Polymers, Biology, Physics, Astronomy, Astrophysics, Chemistry, Operations Research, Economics, Communications, Computer Networks etc., their applications in Chemical Engineering has been relatively meager. 

An artificial neural network (ANN), usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist... 

Keywords Bacterial enhancers Biological computation Gene regulatory networks Synthetic enhancer... 

Merging all these sources led to the development of artificial neural networks as distributed computing systems attempting to implement a greater or smaller part of the functionality characterising biological neural networks. 

The exceptional computational abilities of the human brain have motivated the concept of an NN. The brain can perform certain types of computation, such as perception, pattern recognition, and motor control, much faster than existing digital computers (Haykin, 2009). The operation of the human brain is complex and nonlinear and involves massive parallel computation. Its computations are performed using structural constituents called neurons and the synaptic interconnections between them (that is, a neural network), The development of artificial neural networks is an admittedly approximate attempt to mimic this biological neural network, in order to achieve some of its computational advantages. 

Traditionally focused on evolutionary computation, complex systems, and artificial life, since the last two editions the WTVACE community has been opened to researchers coming from experimental fields such as systems chemistry and biology, origin of life, and chemical and biological smart networks. 

Since biological systems can reasonably cope with some of these problems, the intuition behind neural nets is that computing systems based on the architecture of the brain can better emulate human cognitive behavior than systems based on symbol manipulation. Unfortunately, the processing characteristics of the brain are as yet incompletely understood. Consequendy, computational systems based on brain architecture are highly simplified models of thek biological analogues. To make this distinction clear, neural nets are often referred to as artificial neural networks. 

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