Integrated Development Environment for EEG-Driven Cognitive-Neuropsychological Research

Jacobsen, Shoham; Meiron, Oded; Salomon, David Yoel; Kraizler, Nir; Factor, Hagai; Jaul, Efraim; Tsur, Elishai Ezra

doi:10.1109/jtehm.2020.2989768

Cited by 6 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The review found that the use of neural networks to develop computational architectures is oriented toward the design of the networks, followed by learning algorithms to simulate different brain functions in 38.1% [ 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. Next, the development of brain simulation software is 14.3% [ 49 , 50 , 51 ], and the development of hybrid architectures (using brain computing interfaces supported by neuromorphic processors) accounts for 14.3% [ 52 , 53 , 54 ]. The development and improvement of brain computing interfaces was 9.5% [ 55 , 56 ], as was analysis and database storage through machine learning [ 57 , 58 ].…”

Section: Methods and Resultsmentioning

confidence: 99%

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

Hernández

Barbosa-Santillán

2022

Brain Sciences

View full text Add to dashboard Cite

The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain–computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.

show abstract