Modeling the Mind: A brief review

TL;DR

This paper reviews recent advances in neuroscience and artificial brain research, covering connectionism, neural networks, high-dimensional representations, and biologically inspired cognitive architectures, comparing their strengths and weaknesses.

Contribution

It provides a broad overview of modeling approaches for the brain, highlighting recent developments and contrasting different methodologies and architectures.

Findings

Neuroscience and AI have made significant progress in the past two decades.

Various models differ in their levels of abstraction and methodology.

The review identifies strengths and weaknesses of different brain modeling approaches.

Abstract

The brain is a powerful tool used to achieve amazing feats. There have been several significant advances in neuroscience and artificial brain research in the past two decades. This article is a review of such advances, ranging from the concepts of connectionism, to neural network architectures and high-dimensional representations. There have also been advances in biologically inspired cognitive architectures of which we will cite a few. We will be positioning relatively specific models in a much broader perspective, while comparing and contrasting their advantages and weaknesses. The projects presented are targeted to model the brain at different levels, utilizing different methodologies.