Handbook of Intelligent Computing and Optimization for Sustainable Development. Группа авторов

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Figure 2.8 Multilayer neural network.

Figure 2.8 represents a model of fully connected multi-layer neural network. In this structure, the input layer shown by yellow nodes receive the inputs x₁, x₂, x₃, … …, xN and pass it to the first hidden layer, represented by gray nodes in Figure 2.8. If the model has multiple hidden layers, then the output from the first hidden layer is passed to the next hidden layer and so on. Finally, when the output from the last hidden layer reaches to the output layer represented by the green nodes, it produces the final output y₁, y₂, y₃, … …, yN. It can be said that the multi-layer network consists of a number of single layer network arrange in a cascading manner.

      2.3.6 Learning Process

      Learning is an impressive characteristic of human brain. But, the research on exact process of learning in human nervous system is still in its primary stage. The scientists claim that, learning in biological system occurs due to some alteration of neural structure and synaptic connections. ANN replicates the learning process of human nervous system and the ability to learn is one of the most unique features of ANN. The learning algorithms adjust the weighted connections between the neurons of neural network. The sequence of events occurs during learning process is as follows:

      The purpose of the learning algorithm is to find an appropriate set of weight matrices so that it can generate output efficiently after mapping any input. The three classes of learning processes are listed below.

      So far, we have given a brief overview of ANNs which a significant domain of artificial intelligence. In the next section, we will focus on the core area of this chapter, i.e., ANN using DNA computing. In the sphere of DNA computation, the logical aspect of artificial intelligence has been replaced by chemical properties and characteristics of DNA molecules.

2.4 DNA Neural Networks

      DNA molecules are capable of storing and processing information which stimulates the idea of DNA computing. It is the emerging arena in the domain of computation, and gradually, we are approaching toward the paradigm shift, from silicon to carbon, where DNA computing is overcoming the drawbacks of traditional silicon-based computing. Because of the unique properties (like, Watson-Crick base pairing) of DNA molecules, it has become an influential tool of engineering at nano-scale. The models of ANNs, DNA logic gates, and DNA logic circuits, as illustrated in this chapter, inspire the basic design of DNA computer.

      Set of short DNA sequences, i.e., DNA oligonucleotides can be used to design a model of ANN. These short DNA sequences are used to code input and output signal and to build the basic architecture of the neuron. In the subsequent subsections, we focus on some of the ANN models developed using DNA sequences.

2.4.1 Formation of Axon by DNA Oligonucleotide and Generation of Output Sequence

      Mills [4] has developed a simple model of neural network which is capable to transport signal using DNA oligonucleotides. The states of activity of the neural model are coded by the concentrations of the neuron DNA oligonucleotides. The input and output neurons are coded by single stranded DNA sequences. Partially double-stranded DNA sequences denote the formed axons.

The DNA oligonucleotides that represent the input and output signals are used to encode the axon of the neural model. These oligonucleotides are then temporarily attached to each other by a linker oligonucleotide. The one half of the linker sequence is complementary to the half of output neuron oligonucleotide and other half is complementary to the prefix of input neuron oligonucleotide. Then, T4 DNA ligase joins these two oligonucleotides permanently. The polymerase extension of the linker sequence along with the out sequence is used to protect the output end of linked oligonucleotide. The formation of the axon sequence is explained in Figure 2.9. The rate at which input neuron DNA concentrations lead to the generation of corresponding concentration of output neuron oligonucleotides is governed by the concentration of the axon DNA oligonucleotides. Axon of each neuron can be prepared in the form of partially double-stranded DNA sequences in ideal wet lab environment.