Journal of Brain and Neurology

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Journal of Brain and Neurology 44 7897 074717

ARTIFICIAL-NEURAL-NETWORK-RESEARCH-ARTICLE

 A major reason for this fascination is that ANNs are 'biologically' inspired. they need the apparent ability to imitate the brain's activity to form decisions and draw conclusions when presented with complex and noisy information. However there are vast differences between biological neural networks (BNNs) of the brain and ANN s.
                                       A thorough understanding of biologically derived NNs requires knowledge from other sciences: biology, mathematics and AI . However to know the fundamentals of ANNs, a knowledge of neurobiology isn't necessary. Yet, it's an honest idea to understand how ANNs are derived from real biological neural systems. The soma of the cell body receives inputs from other neurons  They attempt to simulate the structure and performance of biological (real) neurons. Artificial neural models are loosely supported biology since an entire understanding of the behaviour of real neuronal systems is lacking. the purpose is that only a neighborhood of the behavior of real neurons is important for his or her information capacity. Also, it's easier to implement/simulate simplified models instead of complex ones.
ANNs are designed to understand very specific computational tasks problems. they're highly interconnected, parallel computational structures with many relatively simple individual processing elements (Figure 2). A biological neuron either excites or inhibits all neurons to which it's connected, whereas in ANNs. either excitatory or inhibitory neural connections are possible. There are many sorts of neurons in BNN s, whereas in ANN s only certain types are used. it's possible to implement and study various ANN architectures using simulations on a private computer (PC).

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