OVERVIEW OF BIOLOGICAL NEURONS UNIT-1 OVERVIEW OF BIOLOGICAL NEURONS

Human Brain The human brain contains about 10 billion nerve cells, or neurons. On average, each neuron is connected to other neurons through approximately 10,000 synapses. Neural networks are the information processing system, which are constructed and implemented to model the human brain. The main objective of the neural network research is to develop a computational device for modelling the brain to perform various computational tasks at a faster rate than the traditional systems. Artificial neural networks perform various tasks such as pattern-matching and classification, optimization function,approximation,vector quantization, and data clustering. This tasks are very difficult for traditional computers, which are faster in algorithmic computational tasks and precise arithmetic operations. Therefore ,for implementation of artificial neural networks, high speed digital computer are used, which makes the simulation of neural process feasible.

INTERCONNECTIONS IN BRAIN

Biological Neuron Our brains are made up of about 100 billion tiny units called neurons. Each neuron is connected to thousands of other neurons and communicates with them via electrochemical signals. Signals coming into the neuron are received via junctions called synapses, these in turn are located at the end of branches of the neuron cell called dendrites. The neuron continuously receives signals from these inputs and then performs a little bit of magic. What the neuron does (this is over simplified I might add) is sum up the inputs to itself in some way and then, if the end result is greater than some threshold value, the neuron fires. It generates a voltage and outputs a signal along something called an axon.

Artificial neural networks (ANN) is an efficient information processing system which resembles in characteristics with a biological neural network.ANNs posses large number of highly interconnected processing elements called nodes or units or neurons, which usually operate in parallel and are configured in regular architectures. Each neuron is connected with the other by a connection link, each connections link is associated with weights which contain information about the input signal. This information is used by the neuron net to solve a particular problem.

ASSOCIATION OF BIOLOGICAL NET WITH ARTIFICIAL NET

Brain vs. computer –comparison between biological Neuron and artificial Neuron(Brain vs. Computer)

- f å OPERATION OF A NEURAL NET Weighted sum Input vector x Output y Activation function Weight vector w å w0j w1j wnj x0 x1 xn Bias A neuron can have any number of inputs from one to n, where n is the total number of inputs. The inputs may be represented therefore as x1, x2, x3… xn. And the corresponding weights for the inputs as w1, w2, w3… wn. Now, the summation of the weights multiplied by the inputs we talked about above can be written as x1w1 + x2w2 + x3w3 …. + xnwn, which is the activation value. So… a = x1w1+x2w2+x3w3... +xnwn “.

ANN possesses the following characteristics: (1) It is a neurally implemented mathematical model. (2) There exist a large number of highly interconnected processing elements called neurons in an ANN. (3)The interconnections with their weighted linkages hold the informative knowledge. (4)he input signals arrive at the processing elements through connections and connecting weights. (5)The processing elements of the ANN have the ability to learn, recall and generalize from the given data by suitable assignment or adjustment of weights. (6)The computational power cab be demonstrated only by the collective behaviour of neurons, and it should be noted that no single neuron carries specific information. The above –mentioned characteristics make the ANNs as connectionist models, parallel distributed processing models, self organizing system, neuro-computing systems and neuromorphic systems.

Application Scope of Neural Networks ANN has broad application and good scope in the following areas:- (1) Data mining (2)Pattern recognition (3) Future prediction (4) Real time system (5) Forecasting (6) Machine learning

Designing ANN models Designing ANN models follows a number of systemic procedures. In general, there are five basics steps: (1) collecting data (2) pre-processing data (3) building the network (4) train (5) test performance of model

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