Background: The human brain is the most complex system in the known universe, it is
therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However,
until now it has not been understood yet how and why most of these abilities are produced.
Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing
on both understanding the nervous system and, on processing data in a more efficient way than
before. Their aim is to make computers process information similarly to the brain. Important technological
developments and vast multidisciplinary projects have allowed creating the first simulation
with a number of neurons similar to that of a human brain.
Conclusion: This paper presents an up-to-date review about the main research projects that are trying
to simulate and/or emulate the human brain. They employ different types of computational models
using parallel computing: digital models, analog models and hybrid models. This review includes the
current applications of these works, as well as future trends. It is focused on various works that look
for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science
(neuromorphic hardware, machine learning techniques). Their most outstanding characteristics
are summarized and the latest advances and future plans are presented. In addition, this review points
out the importance of considering not only neurons: Computational models of the brain should also
include glial cells, given the proven importance of astrocytes in information processing.