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The Blue Brain Technology Using Wetware Technology and Fuzzy Logic Remya Vinayakumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (1) , 2015, 61-68 The Blue Brain Technology Using Wetware Technology and Fuzzy Logic Remya Vinayakumar Mtech Computer Science And Engineering Department, Sahrdaya College Of Engineering And Technology Asst.Prof. Deepthi Varghese Sahrdaya College Of Engineering And Technology Prof. Vince Paul HOD Computer Science And Engineering Department,Sahrdaya College Of Engineering And Technology Calicut University Malappuram(district), Calicut,Kerala-673635,India. Abstract— " Blue brain " is the name of the world's first Blue brain in the first artificial brain to be developed. The virtual brain. It is a machine that can function as technology that works behind the blue brain is called the human brain.The Blue Brain Project began in July 2005 blue brain technology. The blue brain is created using the as a collaboration between Professor Henry Markram artificial neural network . On 1 July 2005, the Brain Mind from the Brain Mind Institute at the EPFL (Ecole Institute (BMI, at the Ecole Polytechnique Fidrale de Polytechnique Fdrale de Lausanne) and IBM Lausanne) and IBM (International Business Machines) (International Business Machines), aimed at modelling launched the Blue Brain Project. The aims of this ambitious the neocortical column. Today scientists are in research initiative are to simulate the brains of mammals with a high to create an artificial brain that can think, response, level of biological accuracy and, ultimately, to study the take decision, and keep anything in memory. With the steps involved in the emergence of biological intelligence. advancement in technology, human, the ultimate source The Blue Brain Project plans to reverse engineer the human of information and discovery should also be preserved. brain as a supercomputer simulation. It is hoped that a rat In other words, human is does not live for thousands of brain neocortical simulation ( 21 million neurons) will be years but the information in his mind could be saved achieved by the end of 2014. A full human brain simulation and used for several thousands of years. The technology (86 billion neurons) should be possible by 2023 provided helpful in this activity is Blue Brain.The main aim is to sufficient funding is received. Researchers want to use this upload human brain into machine.It can be used for the technology to develop new therapies for the brain diseases development of the human society. and also new computer technologies. The concept of artificial neural network, fuzzy logic and the wetware Keywords—blue brain, artificial neurons, wetware, technology is being used in the creation of the blue brain. fuzzy logic, perceptron, backpropagation II. ARTIFICIAL NEURAL NETWORK I. INTRODUCTION Artificial neural network is an extremely simplified The ability of a man to control the environment in which model of the brain. The building blocks of the neural he lives is what makes him distinctively different from the networks are called the neurons. An artificial neuron is a other species. His intellectual skills place him at the most computational model inspired in the natural neurons. superior level of the animal kingdom. Thus, underlying all Natural neurons receive signals through synapses located human abilities lie the essential attributes of intelligence. on the dendrites or membrane of the neuron. When the Intelligence refers to the ability to understand, think, act, signals received are strong enough ,surpass a certain interpret and predict the future to achieve and handle threshold , the neuron is activated and emits a signal though relationships, concepts etc. It helps in decision making, the axon. This signal might be sent to another synapse, and problem solving, learning and reasoning. Intelligence thus might activate other neurons.Each neuron receives inputs plays a very important role in survival and progress beyond from many other neurons, changes its state base on the the present. current input and send one output signal to many other Technology has been progressing to a great extend such neurons. that even the human brains are being created artificially through the science of artificial intelligence. Artificial The complexity of real neurons is highly abstracted when intelligence is the simulation of intelligence in machines modelling artificial neurons. These basically consist of which makes it behave like a human being. It is the study inputs (like synapses), which are multiplied by weights and design of intelligent agents where an intelligent agent is (strength of the respective signals), and then computed by a a system that perceives its environment and takes actions mathematical function which determines the activation of that maximize its chances of success. the neuron. Another function computes the output of the www.ijcsit.com 61 Remya Vinayakumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (1) , 2015, 61-68 artificial neuron. ANNs combine artificial neurons in order to process information. There are six characteristics of Artificial Neural Network Information is transmitted as a series of electric impulses, which are basic and important for this technology. so-called spikes. The frequency and phase of these spikes 1) The Network Structure encodes the information. In biological systems, one neuron 2) Parallel Processing Ability can be connected to as many as 10,000 other neurons. 3) Distributed Memory Usually, a neuron receives its information from other 4) Fault Tolerance Ability neurons in a confined area, its so-called receptive field. 5) Collective Solution The higher a weight of an artificial neuron is, the 6) Learning Ability stronger the input which is multiplied by it will be. Weights can also be negative, so we can say that the signal is III. PERCEPTRON inhibited by the negative weight. Depending on the weights, Perceptron is the basic unit of the neural network. A the computation of the neuron will be different. By perceptron takes a vector of a real valued input , calculates adjusting the weights of an artificial neuron we can obtain a linear combination of these inputs, and outputs a 1 if the the output we want for specific inputs. But when we have result is greater than some threshold and outputs a -1 if the an ANN of hundreds or thousands of neurons, it would be result is lesser than the threshold. it consists of: quite complicated to find by hand all the necessary weights. But we can find algorithms which can adjust the weights of • N inputs, ... the ANN in order to obtain the desired output from the • Weights for each input, ... network. This process of adjusting the weights is called • A bias input (constant) and associated weighted sum of learning or training. inputs, y = + + ... + • A threshold function, i.e 1 if y > 0, -1 if y <= 0 Fig.1 Biological Neural Network The number of types of ANNs and their uses is very high. Since the first neural model by McCulloch and Pitts (1943) there have been developed hundreds of different Fig.3 A Perceptron models considered as ANNs. The differences in them might be the functions, the accepted values, the topology, the learning algorithms, etc. Also there are many hybrid models IV. THE BACKPROPAGATION ALGORITHM where each neuron has more properties than the ones we are The backpropagation algorithm is used in layered feed- reviewing here. Because of matters of space, we will forward ANNs. This means that the artificial neurons are present only an ANN which learns using the organized in layers, and send their signals forward, and then backpropagation algorithm for learning the appropriate the errors are propagated backwards. The network receives weights, since it is one of the most common models used in inputs by neurons in the input layer, and the output of the ANNs, and many others are based on it. Since the function network is given by the neurons on an output layer. There of ANNs is to process information, they are used mainly in may be one or more intermediate hidden layers. fields related with it. There are a wide variety of ANNs that The backpropagation algorithm uses supervised are used to model real neural networks, and study behaviour learning, which means that we provide the algorithm with and control in animals and machines, but also there are examples of the inputs and outputs we want the network to ANNs which are used for engineering purposes, such as compute, and then the error (difference between actual and pattern recognition, forecasting, and data compression. expected results) is calculated. The idea of the back propagation algorithm is to reduce this error, until the ANN learns the training data. The training begins with random weights, and the goal is to adjust them so that the error will be minimal. The activation function of the artificial neurons in ANNs implementing the backpropagation algorithm is a weighted sum (the sum of the inputs xi multiplied by their respective weights ): , Fig.2 Artificial Neural Network www.ijcsit.com 62 Remya Vinayakumar et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (1) , 2015, 61-68 We can see that the activation depends only on the inputs And then, how much the output depends on the and the weights. If the output function would be the identity activation, which in turn depends on the weights (from (1) (output=activation), then the neuron would be called linear. and (2)): But these have severe limitations. The most common output function is the sigmoidal function: 1 ∆ 1 , 2 1, And we can see that (from (6) and (7)): The sigmoidal function is very close to one for large positive numbers, 0.5 at zero, and very close to zero for 2 1 large negative numbers. This allows a smooth transition 8 between the low and high output of the neuron (close to zero or close to one).
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