Brain neural network model

Neural networks model the functionality of the brain. They learn from examples, whereby the weights of the neurons are adapted on the basis of training data. 

Garg P, Verma J (2006) In silico prediction of blood-brain barrier permeability An artificial neural network model. J Chem Inf Model 46 289-297... 

Hemmateenejad, B., Miri, R., Safarpour, M.A., Mehdipour, A.R. Accurate prediction of the blood-brain partitioning of a large set of solutes using ab initio calculations and genetic neural network modeling. J. Comput. Chem. 2006, 27, 1125-35. 

Dorronsoro, I., Ghana, A., Abasolo, M.I., Castro, A., Gil, C., Stud, M. and Martinez, A. (2004) CODES/ neural network model a usefid tool for in silico prediction of oral absorption and blood—brain barrier permeability of structurally diverse drugs. QSAR Comb. Sci., 23, 89-98. 

Neural network models -- and brains -- contain sets of elements, each of which is computationally simple. The elements, called "neurons", are highly interconnected to one another in the human brain there are about 100 billion neurons, and each one is connected to about 10,000 other neurons. Neural network models often contain large numbers of simulated neurons, but not as many as are in the brain. For the remainder of this paper, we will refer to simulated neurons as "units". 

Derived from cognitive scientists attempts to model the structure and organization of the human brain, neural network simulations are powerful tools to deal with... 

A human brain continually receives input signals from many sources and processes them in parallel to create appropriate output responses. There are billions of neurons in the brain that interconnect in a myriad of ways to form elaborate neural networics. ANN are an attempt to process information efficiently and quickly using brain nenral networics as a model. Like brain neural networks, ANN have many neuron-like nodes that interact with one another, like brain neural networks, ANN must undergo a learning process before they are ready to process information automatically like brain nenral networks, ANN take information from a number of primary inputs and form useful outputs. 

Fabric engineering involves a great deal of expertise and experience. It needs a thorough understanding of the functional properties and their key control constmction parameters. When the relationship between a set of interrelated properties goes beyond the complete comprehension of the human brain, neural networks could be used to find the unknown function. The utility performance properties of woven fabric depend on the combined effect of the properties of the constituent fibers, yam and fabric stracture. According to Behera and Karthikeyan [22], the relationship between stmcture and properly of the fabric is complex and inherently nonlinear and to create a predictive model, one must resolve the complexities. 

In the next step, it leads to the creation of an artificial neural network, which is formed by connecting the output channels of a neuron to the input channels of other neurons. Additionally, external input channels may be connected to some of the input channels of neurons. Therefore the entire network performs a more enhanced nonlinear conversion of input vector U into output vector Y. While the neural network model is not a model of the human brain, what is biologically... 

Problems involving routine calculations are solved much faster and more reliably by computers than by humans. Nevertheless, there are tasks in which humans perform better, such as those in which the procedure is not strictly determined and problems which are not strictly algorithmic. One of these tasks is the recognition of patterns such as feces. For several decades people have been trying to develop methods which enable computers to achieve better results in these fields. One approach, artificial neural networks, which model the functionality of the brain, is explained in this section. 

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. 

In Chapter 43 the incorporation of expertise and experience in data analysis by means of expert systems is described. The knowledge acquisition bottleneck and the brittleness of domain expertise are, however, the major drawbacks in the development of expert systems. This has stimulated research on alternative techniques. Artificial neural networks (ANN) were first developed as a model of the human brain structure. The computerized version turned out to be suitable for performing tasks that are considered to be difficult to solve by classical techniques. 

Any software model of the brain that we could create using a current PC could emulate only an infinitesimal amount of it, so it might seem that our expectations of what an ANN could accomplish should be set at a thoroughly modest level. Happily, the reality is different. Though computer-based neural networks are minute compared with the brain, they can still outperform the brain in solving many types of problems. 

In the human brain, it is the combined efforts of many neurons acting in concert that creates complex behavior this is mirrored in the structure of an ANN, in which many simple software processing units work cooperatively. It is not just these artificial units that are fundamental to the operation of ANNs so, too, are the connections between them. Consequently, artificial neural networks are often referred to as connectionist models. 

The rather time- and cost-expensive preparation of primary brain microvessel endothelial cells, as well as the limited number of experiments which can be performed with intact brain capillaries, has led to an attempt to predict the blood-brain barrier permeability of new chemical entities in silico. Artificial neural networks have been developed to predict the ratios of the steady-state concentrations of drugs in the brain to those of the blood from their structural parameters [117, 118]. A summary of the current efforts is given in Chap. 25. Quantitative structure-property relationship models based on in vivo blood-brain permeation data and systematic variable selection methods led to success rates of prediction of over 80% for barrier permeant and nonper-meant compounds, thus offering a tool for virtual screening of substances of interest [119]. 

Artificial neural networks arose from efforts to model the functioning of the mammalian brain. The most popular ANN — the feedforward ANN — has deeper roots in statistics than in neurobiology, though. A form of ANN (a Probability Neural Network) has been used within a QPA context to improve sensor data reliability, but not as an on-line quality model [57]. The best way to represent a feedforward ANN as an on-line quality model for SHMPC is... 

Developed several decades ago, ANNs are being increasingly applied to the development and application of quantitative prediction models.43 15 ANNs simulate the parallel processing capabilities of the human brain, where a series of processing rmits (aptly called neurons ) are used to convert input variable responses into a concentration (or property) output. Neural networks cover a very wide range of techniques that are used for a wide range of applications. 

---