Skip to Main content Skip to Navigation
Journal articles

Characterizing Self-Developing Biological Neural Networks: A First Step Towards their Application To Computing Systems

Hugues Berry 1 Olivier Temam 1
1 ALCHEMY - Architectures, Languages and Compilers to Harness the End of Moore Years
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France
Abstract : Carbon nanotubes are often seen as the only alternative technology to silicon transistors. While they are the most likely short-term one, other longer-term alternatives should be studied as well. While contemplating biological neurons as an alternative component may seem preposterous at first sight, significant recent progress in CMOS-neuron interface suggests this direction may not be unrealistic; moreover, biological neurons are known to self-assemble into very large networks capable of complex information processing tasks, something that has yet to be achieved with other emerging technologies. The first step to designing computing systems on top of biological neurons is to build an abstract model of self-assembled biological neural networks, much like computer architects manipulate abstract models of transistors and circuits. In this article, we propose a first model of the structure of biological neural networks. We provide empirical evidence that this model matches the biological neural networks found in living
organisms, and exhibits the small-world graph structure properties
commonly found in many large and self-organized systems, including
biological neural networks. More importantly, we extract the simple
local rules and characteristics governing the growth of such networks, enabling the development of potentially large but realistic biological neural networks, as would be needed for complex information processing/computing tasks. Based on this model, future work will be targeted to understanding the evolution and learning properties of such networks, and how they can be used to build computing systems.
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download

https://hal.inria.fr/inria-00000024
Contributor : Hugues Berry <>
Submitted on : Tuesday, May 10, 2005 - 8:31:32 PM
Last modification on : Wednesday, September 16, 2020 - 5:04:15 PM
Long-term archiving on: : Thursday, April 1, 2010 - 9:27:13 PM

Identifiers

Collections

Citation

Hugues Berry, Olivier Temam. Characterizing Self-Developing Biological Neural Networks: A First Step Towards their Application To Computing Systems. Lecture Notes in Computer Science, Springer, 2005, 3512, pp.306-317. ⟨inria-00000024⟩

Share

Metrics

Record views

637

Files downloads

1146