of the brain to use Boolean algebra. As the input and output of the neural model are binary numbers, thus the multi-layer neural network can implement the basic logic gates, i.e., AND, OR, and NOT. This can be achieved by appropriately choosing the weights. In this chapter, we illustrate the design strategy of logical gates using the secondary structures of DNA molecules. DNA logic circuits, which are the alternatives to complex Boolean circuits, can be developed by the suitable use of DNA oligonucleotides and various enzymes. These DNA logic gates and logic circuits can be the pillars of a competent DNA computer in near future.
We also demonstrate the applications of DNA logic gates in real life. The successful development of DNA logic circuits accomplishes the basic requirement to design nano-DNA-devices. In the forthcoming generation, these nano-machines can be implanted in living organisms so that it could sense the conditions and accordingly can make decisions and respond to the situation. Based on the sensed circumstances, the nano-devices would be able to take required actions, for example, releasing medicines and killing hazardous cells.
In Section 2.2 of this chapter, we give a brief overview of biological neurons which is crucial to build the concept of ANN. Section 2.3 focuses on the short description of ANN which has been illustrated as the mathematical and computational tool for nonlinear statistical data modeling. DNA neural network is demonstrated in Section 2.4. Section 2.5 contains the developments on DNA logic gates, DNA logic circuits, and their applications. Finally, Section 2.6 concludes this chapter by focusing on the advent of DNA-based artificial intelligence.
2.2 Biological Neurons
The basic component of nervous system is the specialized cells called neuron. It is actually an information processing unit which is responsible for receiving and transmitting information. About a hundred billion neurons are connected to thousands of its neighbor neurons and through these interactions the communication of information occurs throughout the body. Because of the enormous interactions between the neurons and their parallel processing which controls organized brain functions, the network is termed as neural network. Figure 2.1 presents the schematic diagram of biological neuron.
Figure 2.1 Schematic diagram of biological neuron.
The general structural parts of the neuron and their functions are briefly discussed below:
• Dendrites: These are the branched extensions at the beginning of neuron. Dendrites are covered with synapses.■ Function:i. increases the surface area of the cell body;ii. the synapses receive information in form of electrochemical signal from other neurons and transmit it to the cell body, soma.
• Soma: It is the spherical shaped cell body of the neuron which contains the nucleus. It is the connector between dendrites and axon of the neuron. It does not take active role in information transmission.■ Function:i. it produces all the proteins required for the axons, dendrites, and synaptic terminals;ii. contains the cell organelles, viz., Golgi apparatus, mitochondria, endoplasmic reticulum, secretory granules, polysomes, and ribosomes;iii. generates neurotransmitters and keeps the neuron active.
• Axon hillock: This specialized part of the cell body connects the soma to the axon which is the site of summation for incoming electrochemical signal.■ Function:i. The neuron has a particular threshold for incoming electrochemical signal. If it is exceeded, the axon hillock produces a signal, termed as action potential, down the axon.
• Axon: It is also termed as nerve fiber. It is the elongated projection from cell body to the terminal endings. The speed of transmission of the electrochemical signal, i.e., the information, is directly proportional to the axon length.■ Function:i. The neuron has a particular threshold for incoming electrochemical signal. If it is exceeded, the axon hillock produces a signal, termed as action potential, down the axon.
• Myelin Sheath: Some axons are covered with lipid-rich, i.e., fatty, insulating layer called myelin. Sometimes, gaps exist between the myelin sheaths along the axon.■ Function:i. it protects the axon;ii. it is the electrical insulator of the neuron, i.e., it blocks the electrical impulses traveling through itself;iii. it prevents depolarization;iv. as the electrical impulses cannot pass through the sheath, it jumps from a gap between the sheaths to another gap, and thus, the myelin sheath speeds up the transmission of the signal along the neuron efficiently.
• Nodes of Ranvier: The uninsulated, ion-rich gaps between myelin sheaths, which are approximately 1 μm wide, are called the nodes of Ranvier.■ Function:i. it mediates the exchange of certain ions, like sodium and chloride;ii. helps in rapid transmission of action potential along the axon
• Terminal Buttons: Small knob-like structures located at the end of the neuron is termed as terminal buttons. It contains vesicles containing neurotransmitter.■ Function:i. It covert the electrical impulses into chemical signal. When the electrical impulses reach at these buttons, neurotransmitter is secreted which sends the electrochemical signal to other neurons.
• Synapse: The gap between two neurons or a neuron and a gland or a muscle is called synapse.■ Function:i. It transmits the electrochemical signal from one cell to another cell.
The propagation of signal through neurons and resemblance with artificial neurons are shown in Figure 2.2.
Figure 2.2 Propagation of signal through neurons.
ANN, which is a domain of artificial intelligence, mimics the above discussed biological neural networks of nervous system. The connections of the neurons in ANN are computationally and mathematically modeled in more or less same way as the connections between the biological neurons.
In the following section, we highlight the basic network topology and different types of models in ANN and the learning rules.
2.3 Artificial Neural Networks
An ANN can be defined as a mathematical and computational tool for nonlinear statistical data modeling, influenced by the structure and function of biological nervous system. A large number of immensely interconnected processing units, termed as neurons, build ANN.
Generally, ANN receives a set of inputs and produces the weighted sum, and then, the result is passed to the nonlinear function which generates the output. Like human being, ANN also learns by example. The models of ANN are required to be appropriately trained to generate the output efficiently. In biological nervous system, learning involves adaptations in the synaptic connections between the neurons. This idea influences the learning procedure of ANN. The system parameter of ANNs can be adjusted according to I/O pattern. Through learning process, ANN can be applied in the domains of data classification, pattern recognition, etc.
The researchers are working on ANN for past several
