Learning and Hopfield Networks Essay Example

The neural network functioning is primarily based on the functional characteristics of the human brain network. The interconnect neurons in the brain system comprise of the axons, neurons, and synapsis that are the elements constituting the biological network of the brain. The learning and memory characteristics are primarily associated with the mind. Conversely, they obtain significantly different goals. Learning involves a permanent transformation to a person's behavior that results from their experiences. Equally, memory consists of the storage of information, encoding, and recovering information, hence permitting that particular set of data to be available for recall. Learning and memory occur in various methods, and they can entail a comprehensive and systematic approach referred to as Hopfield networks. Therefore, the essay will present an argument discussing the significant analytical function of the Hopfield networks that share a characteristic of learning in the brain-mind system.

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While researchers and scientists developed an increasing interest in the development of artificial intelligence, the primary focus was on the devices, structures, and networks that are essential techniques that the human brain functions. It is critical to study in details the biology of the human brain while creating and structuring an intelligent system. Notably, the neurobiology discipline that provides a basis for scientists of artificial intelligence concentrates on recreating. John Hopfield was a great researcher and scientist who had a particular interest in artificial intelligence.

In 1992, John Hopfield established the Hopfield network that provided researchers and psychologists with an ideal model that allowed them to study further and comprehend human memory. Hopfield network consists of artificial neurons that can store a significant amount of memories that are similar to the brain. The design of the system can be shown when a section of the information stored is resented (Sima, Orponen & Antti-Poika, 2000). Moreover, there is a considerable level of stability in the network system that shows when a small number of connections between neurons tempered with where the recalled memory is not adversely corrupted, and the network can respond within a best guess scenario. Particular procedures are essential when storing information the memory of the Hopfield network, which consists of the learning process. It occurs when a specific amount of weight is updated through the existing data by the neurons situated on both sides of the connection for the particular weight (Sathasivam & Abdullah, 2008).

Double or binary techniques of computing primarily based on 0 or 1, or an off or on the system. The Hopfield networks utilize the double units to show 1 or -1 with 1 and 0 for each basis. Therefore, the network is dependent only when the input into a component surprises the output of the extreme level. The repetitive nature of the neural network is intended to collect binary input from the majority of the neural networks, conflicting with computers, they are not developed to exercise simplicity as the binary code (Kobayashi, 2017). The thresholds of the neural networks receive significant quantities in the span of negative and positive infinity; hence, it is vital to convert the binary numbers 1 and -1 to a standard range.

The energy models that are a property of Hopfield networks derived from the global energy function. The Hopfield network is characterized as an elementary, energy-based model that contains a binary threshold element that acquires repeated links. The recurring types of systems that obtain non-linear units are often more multifaceted since they tend to behave in various forms. Nonetheless, Hopfield assumed that when specific connections are proportionate in a natural sense, they might result in a global energy function (Kobayashi, 2017). Every neuron contains a particular binary code in a unique development. Hopfield showed that the binary threshold assessment law permits for a descending motion of energy. When the actual implementation of the regulation happens, the least amount of energy emerges. Hopfield demonstrates that global energy is a result of the influence, where each differs depending on connection weight and the binary unit nature of the neurons that interrelate.

Although some researchers may argue that Hopfield network when compared to other scientific models, it may show an unrefined and simplified model of what happens in the human brain. A close look at the model, merely regulating the input and output in the model, it can show the basic foundation of what happens when a single neuron interacts with another. Moreover, other output variables that get the capability to significantly change the output of the Hopfield network system, making the prototype somewhat inferior compared to what occurs about neurobiology. For instance, the variable that might influence the input and output equations in the human biological system comprises of the propaganda interruptions, location dependent, and intracellular capacities. This dramatically impacts the critical elements coupled with other associated variables since they are vital factors that play a crucial purpose in determining the functions of the human brain (Kumar & Singh, 2011).

The Hopfield networks significantly share elements of the learning in the mind system regardless of the many understatements regarding its intellectual fundamentals. It is useful, adequate and appropriate models that provide a deeper understanding of the operations of the neurons and result in a complex computation (Hopfield, 1994). Furthermore, Hopfield networks compared to other scientific models, it is easier to operate using optical or electrical techniques. He recognized the connection between several elements amongst the physical system replicated amongst neural networks and arithmetic procedure when he developed his model (Murthy & Gabbouj, 2015). While developing a suitable model, Hopfield gave computer science researchers a method in which they could examine neural networks.

It may be hard to attain a perfect level in the current and future neural network models in producing a system that would function as a replicate of a human brain. The Hopfield networks are fundamental in demonstrating the process of learning in a simpler form. It shows the practicality in its application as a model, and it can significantly differ contingent to the category of science that uses the paradigm. Though Hopfield networks have various advantages to researchers, neurobiologists might lag in fully understanding the benefits that the model can offer since it is binary and might omit other vital elements. Nevertheless, it is principally appropriate to artificial intelligence as a critical advancement to reconstruct the human mind.

Conclusion

Hopfield networks provide an essential model to both scientists and psychologists that gives a unique understanding of the complex systems such as the human brain. The models that represent the biological system of the functioning of the human brain have disadvantages; the Hopfield networks have more advantages than their weaknesses. However, some branches of science may find Hopfield networks more valuable compared to other fields; the model gives researchers a unique experience replicating the functionality of the human brain system. A considerable number of computer scientists seem to adhere to netw as the motivating implement while trying to clarify extra systems in their research field.

References

Hopfield, J. J. (1994). Neurons, dynamics, and computation. Physics Today, 47(2), 40-49.

Kobayashi, M. (2017). Symmetric Complex-Valued Hopfield Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 28(4), 1011-1015. doi:10.1109/tnnls.2016.2518672

Kumar, S., & Singh, M. P. (2011). Performance evaluation of Hopfield neural networks for overlapped English characters by using genetic algorithms. International Journal of Hybrid Intelligent Systems, 8(4), 169-184. doi:10.3233/his-2011-0138

Murthy, G. R., & Gabbouj, M. (2015). On the design of Hopfield Neural Networks: Synthesis of Hopfield type associative memories. 2015 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2015.7280299

Sathasivam, S., & Abdullah, W. A. (2008). Logic Learning in Hopfield Networks. Modern Applied Science, 2(3). doi:10.5539/mas.v2n3p57

Sima, J., Orponen, P., & Antti-Poika, T. (2000). On the Computational Complexity of Binary and Analog Symmetric Hopfield Nets. Neural Computation, 12(12), 2965-2989. doi:10.1162/089976600300014791