Biological neural networks

A number of researchers have tried training neural networks to achieve color constancy. A neural network basically consists of a set of nodes connected by weights (McClelland and Rumelhart 1986 Rumelhart and McClelland 1986 Zell 1994). Artificial neural networks are an abstraction from biological neural networks. Figure 8.2 shows a motor neuron in (a) and a network of eight artificial neurons on the right. A neuron may be in one of... 

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... 

Beer, R.D., Ritzmann, R.E., and McKenna, T. 1993. Biological Neural Networks in Invertebrate Neuroethology and Robotics, Academic Press, New York. 

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... 

Biological neural networks are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis. 

Arbas, E.A., Willis, M.A. Kanzaki, R. 1993. Organization of goal-oriented locomotion Pheromone-modulated flight behavior of moths. In Biological Neural Networks in Invertebrate Neuroethology and Robotics (Ed. by R.D. Beer, R.E. Ritzmann and T. McKenna), pp 159—198. San Diego Academic Press. 

DeBusschere and Kovacs [28] developed a portable microfluidic platform integrated with a complementary metal-oxide semiconductor (CMOS) chip which enables control of temperature as well as the capacity to measure action potentials in cardiomyocytes. When cells were stimulated with nifedipine (a calcium channel blocker), action potential activity was interrupted. Morin et al. [29] seeded neurons in an array of chambers in a microfluidic network integrated with an array of electrodes (Fig. 5b). The electrical activity of cells triggered with an electrical stimulus was monitored for several weeks. Cells in all chambers responded asynchronously to the stimulus. This device illustrates the utility of microfluidic tools that can investigate structure, function, and organization of biological neural networks. A similar study probed the electrical characteristics of neurons as they responded to thermal stimulation [30] in a microfluidic laminar flow. Neurons were seeded on an array of electrodes (Fig. 5c) which allowed for measurements of variations in action potentials when cells were exposed to different temperatures. 

The structure of ANNs is reminiscent of biological neural networks. Several simple nodes ( neurons ) are connected to form a network of nodes. Algorithms define the strengths between the neurons. Multilayer preceptron (MLP) is one of the most popular and traditional models of ANNs [15]. Its structure consists of an input layer, one or more hidden layers, and an output layer. All layers contain a variable number of neurons. The network maps the input data (e.g., UV-Vis data) to a set of outputs (e.g., concentrations). A training data set can be used to optimize the strength of the connections. The trained network is then able to make predictions for a validation data set. 

The artificial neural network is a simulation and approximation of the biological neural network, and simulates the biological neural networks from the structure, mechanism and function. From the viewpoint of the system, the artificial neural network is an adaptive nonlinear dynamic system constituted by a large number of neurons through extremely rich and perfect connection. From the viewpoint of the system, the artificial neural network is an adaptive nonlinear dynamic system constituted by a large number of neurons through extremely rich and perfect connection. 

The driving force behind the development of the ANN models is the biological neural network, a complex structure, which is the information processing system for a living being. Thus ANN mimics a human brain for solving complex problems, which may be... 

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. 

Whereas the former requirements are inspired by real, biological neural networks, the latter one has been introduced for purely technical reasons — to make a formal description of the artificial neural network easier. On the other hand, that condition does not actually impose any real restriction on the ANN, in the sense that any architecture in which it does not hold can be extended to an architecture in which it does hold, while between any two neurons from the original, non-extended architecture, a connection exists in the extended architecture if and only if it already exists in the original one. 

The artificial neural network (ANN) is a system imitating the operation of a biological neural network. It is composed of the set of basic elements (artificial neurons) that are mutually connected. In general, to describe the ANN operation at least three basic properties should be known namely a neuron model (transfer function), the network topology and the method of training. 

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. 

Biological neural networks can, on the other hand, perform these tasks apparently effortlessly using myriads of nerve cells (neurons). Current estimates place the number of neurons in the human brain at 10 . They are organized in a complex unmapped interconnection structure in which each neuron may be connected via variably weighted links (s3mapses) to several thousand other neurons. The information learnt by the network is stored in these s)mapses and processed collectively by the whole net This densely interconnected structure allows the brain to explore many competing hypotheses simultaneously. 

---