Abstract

Different research projects around the world are trying to emulate the human brain. They employ diverse types of computational models: digital models, analog models and hybrid models. This communication includes a summary of some main projects, as well as future trends in this subject. 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). In addition, given the proven importance of glial cells in information processing, the importance of considering astrocytes into the brain computational models is pointed out.

Highlights

  • The first computational brain models were created with the goal of reproducing this extraordinary organ, in order to understand and mimic the way the information is processed, as well as its energy efficiency [1,2,3,4,5,6,7,8,9]

  • Basically two scientific disciplines emerge: the connectionism branch of Artificial Intelligence, which is aimed at developing algorithms based on neural networks to process the information, and Computational Neuroscience which seeks to create realistic models of the brain

  • It should be pointed out that until now in most computational brain models the capacity to process the information from the other half of the brain, containing 84 billion glial cells [13], has not been taken in consideration

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Summary

Introduction

The first computational brain models were created with the goal of reproducing this extraordinary organ, in order to understand and mimic the way the information is processed, as well as its energy efficiency [1,2,3,4,5,6,7,8,9]. At the end of the eighties, Carver Mead [10, 11] proposed the concept of Neuromorphic Engineer to describe the use of Very Large Scale Integration (VLSI) systems which contained analog circuits to mimic the neurons All these scientific disciplines have tried to model the brain in one way or another. Some works focused on implementing artificial astrocytes in the brain models are referenced

Models Classification
Characteristics of the models
Brain Computational Models with Glia
Conclusions
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