Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

Area of Science:

• Computational Neuroscience
• Cognitive Science
• Information Technology

Background:

• The brain's complexity has been studied from philosophical to computational perspectives.
• Advancements in imaging, digital platforms, and processing power enhance brain function studies.
• Information technology (IT) supports deep knowledge of brain mechanisms and artificial intelligence (AI).

Purpose of the Study:

• To review and analyze computational architectures for brain activity simulation using neural networks.
• To determine current research objectives and main lines in neural networks for brain study.
• To foster interdisciplinary collaboration in computational neurosciences.

Main Methods:

• Systematic literature review of three computational science databases (EbscoHost Web, IEEE Xplore, Compendex Engineering Village).
• Analysis of research articles focusing on neural networks and computational architectures for brain simulation.
• Identification of key findings and research trends in computational neurosciences.

Main Results:

• Neural networks form the basis for many computational architectures.
• Techniques like neuromorphic chips, MRI, and brain-computer interfaces (BCI) enhance brain activity simulation.
• Current research focuses on replicating complex human tasks, including consciousness, using AI.

Conclusions:

• Neural networks are pivotal in advancing computational neurosciences and AI development.
• Interdisciplinary approaches are crucial for understanding brain functions and developing sophisticated AI.
• Future research directions involve enhancing computational architectures for more complex brain simulations.