Brain architecture: A design for natural computation

TL;DR

This paper reviews current understanding of brain architecture, highlighting how neural network organization supports robustness and efficiency, and explores how these principles could inspire future computer designs.

Contribution

It provides an updated overview of neural system organization, emphasizing spatial and topological features that enhance robustness and processing speed, and discusses self-organization mechanisms.

Findings

Neural networks exhibit robustness against failures.

Brain architecture facilitates fast processing.

Self-organization mechanisms shape neural networks.

Abstract

Fifty years ago, John von Neumann compared the architecture of the brain with that of computers that he invented and which is still in use today. In those days, the organisation of computers was based on concepts of brain organisation. Here, we give an update on current results on the global organisation of neural systems. For neural systems, we outline how the spatial and topological architecture of neuronal and cortical networks facilitates robustness against failures, fast processing, and balanced network activation. Finally, we discuss mechanisms of self-organization for such architectures. After all, the organization of the brain might again inspire computer architecture.