Modeling self-developing biological neural networks

Authors:
Hugues Berry;Olivier Temam
Affiliations:
Alchemy group, INRIA Futurs, Parc Club Orsay Université, ZAC des vignes, 4 rue Jacques Monod, 91893 Orsay Cedex, France;Alchemy group, INRIA Futurs, Parc Club Orsay Université, ZAC des vignes, 4 rue Jacques Monod, 91893 Orsay Cedex, France
Venue:
Neurocomputing
Year:
2007

Citing 4
Cited 1

NanoFabrics: spatial computing using molecular electronics

ISCA '01 Proceedings of the 28th annual international symposium on Computer architecture
Modeling of growing networks with directional attachment and communities

Neural Networks
Evolution and Structure of the Internet: A Statistical Physics Approach

Evolution and Structure of the Internet: A Statistical Physics Approach
Nanowire-based programmable architectures

ACM Journal on Emerging Technologies in Computing Systems (JETC)

Automatic abstraction and fault tolerance in cortical microachitectures

Proceedings of the 38th annual international symposium on Computer architecture

Quantified Score

Hi-index	0.01

Visualization

Abstract

Recent progress in chips-neuron interface suggests real biological neurons as long-term alternatives to silicon transistors. The first step to designing such computing systems is to build an abstract model of self-assembled biological neural networks, much like computer architects manipulate abstract models of transistors. In this article, we propose a model of the structure of biological neural networks. Our model reproduces most of the graph properties exhibited by Caenorhabditis elegans, including its small-world structure and allows generating surrogate networks with realistic biological structure, as would be needed for complex information processing/computing tasks.