The humankind seems to have made a huge leap in its study of neuroscience and deep learning since the concept of artificial intelligence no longer represents a figment of science fiction. The concept of artificial neural networks (ANN) as a method of imitating the functioning of the human brain is currently defined as “a tool for predicting and modeling the phenomena that is inspired by the human brain functions” (Vakili et al. 40). Therefore, the process of an ANN’s functioning is based on learning by making a series of connections between different pieces of data.
The framework for an ANN’s functioning is represented in the ANN architecture, in which neurons travel to connect with each other and form a sequence that will represent a piece of information. In an ANN, there is an input and output connection for each neuron, which allows simulating the processes that occur during information processing in a human brain. As the specified connections are made, they are assigned a specific value and a factor, which, when multiplied, define its general weight (Koyuncu 5). As a result, an architecture of knowledge is constructed.
However, the specified structure does not stay intact but is shaped continuously based on the development of new connections and the introduction of new information. Thus, ANNs mimic the processes taking place in the human brain (Wanto et al. 48). As a result, each ANN becomes eventually a contraption of multiple levels of neurons, becoming highly flexible and adaptable. The specified property of ANNs creates the platform for learning by adding new connections, restructuring the existing ones, removing the old ones, and making other necessary changes. BY mimicking the human brain, ANNs provide a chance to explore the concept of deep learning and its algorithms.
Works Cited
Koyuncu, Ismail. “Implementation of High Speed Tangent Sigmoid Transfer Function Approximations for Artificial Neural Network Applications on FPGA.” Advances in Electrical and Computer Engineering, vol. 18, no. 3, 2018, pp. 1-8. Web.
Vakili, M., et al. “A Hybrid Artificial Neural Network-Genetic Algorithm Modeling Approach for Viscosity Estimation of Graphene Nanoplatelets Nanofluid Using Experimental Data.” International Communications in Heat and Mass Transfer, vol. 82, 2017, pp. 40-48. Web.
Wanto, Anjar, et al. “Use of Binary Sigmoid Function and Linear Identity in Artificial Neural Networks for Forecasting Population Density.” International Journal of Information System & Technology, vol. 1, no. 1, 2017, pp. 43-54. Web.