Many advances have been made in the last decade in the understanding of the computational principles underlying olfactory system functioning. Neuromorphic Olfaction is a collaboration among European researchers who, through NEURO-CHEM (Fp7-Grant Agreement Number 216916)–a challenging and innovative European-funded project–introduce novel computing paradigms and biomimetic artifacts for chemical sensing. The implications of these findings are relevant to a wide audience, including researchers in artifical olfaction, neuroscientists, physiologists, and scientists working with chemical sensors.

Developing neuromorphic olfaction from conceptual points of view to practical applications, this cross-disciplinary book examines:

• • The biological components of vertebrate and invertebrate chemical sensing systems
• • The early coding pathways in the biological olfactory system, showing how nonspecific receptor populations may have significant advantages in encoding odor intensity as well as odor identity
• • The redundancy and the massive convergence of the olfactory receptor neurons to the olfactory bulb
• • A neuromorphic approach to artificial olfaction in robots
• • Reactive and cognitive search strategies for olfactory robots
• • The implementation of a computational model of the mammalian olfactory system

The book’s primary focus is on translating aspects of olfaction into computationally practical algorithms. These algorithms can help us understand the underlying behavior of the chemical senses in biological systems. They can also be translated into practical applications, such as robotic navigation and systems for uniquely detecting chemical species in a complex background.

Contents

• Series Preface
• Preface
• About the Editors
• Contributors
• 1. Engineering Aspects of Olfaction
  Krishna C Persaud.
  • ABSTRACT
  • 1.1 INTRODUCTION
  • 1.2 ODORANTS, CHEMICAL STIMULI, AND ODORANT BINDING SITES
  • 1.3 ODOR SAMPLE
  • 1.4 OLFACTORY SYSTEMS
  • 1.6 TOWARD BIOLOGICALLY INSPIRED ARTIFICIAL OLFACTION
  • ACKNOWLEDGMENT
  • REFERENCES
• 2. Study of the Coding Efficiency of Populations of Olfactory Receptor Neurons and Olfactory Glomeruli
  Agustín Gutiérrez-Gálvez and Santiago Marco.
  • 2.1 INTRODUCTION
  • 2.2 STUDY OF ODOR INTENSITY CODING IN AN ORN POPULATION
  • 2.3 STUDY OF ODOR INTENSITY CODING IN THE GLOMERULI
  • 2.4 STUDY OF ODOR INTENSITY/IDENTITY CODING IN AN ORN POPULATION
  • 2.5 CONCLUSIONS
  • REFERENCES
• 3. Mimicking Biological Olfaction with Very Large Chemical Arrays
  Mara Bernabei, Romeo Beccherelli, Emiliano Zampetti, Simone Pantalei, and Krishna C Persaud.
  • ABSTRACT
  • 3.1 INTRODUCTION
  • 3.2 ARTIFICIAL OLFACTORY SYSTEMS
  • 3.3 CONCEPT AND SYSTEM ARCHITECTURE
  • 3.4 CROSS TALK COMPENSATION
  • 3.5 FABRICATION OF THE RESISTIVE SENSOR ARRAY DIES
  • 3.6 POLYMER DEPOSITION
  • 3.7 LABORATORY EXPERIMENTS
  • 3.8 CONCLUSION
  • Acknowledgment
  • REFERENCES
• 4. The Synthetic Moth: A Neuromorphic Approach toward Artificial Olfaction in Robots
  Vasiliki Vouloutsi, Lucas L Lopez-Serrano, Zenon Mathews, Alex Escuredo Chimeno, Andrey Ziyatdinov, Alexandre Perera i Lluna, Sergi Bermúdez i Badia, and Paul F. M. J Verschure.
  • 4.1 INTRODUCTION
  • 4.2 THE MOTH
  • 4.3 METHODS
  • 4.4 RESULTS
  • 4.5 CONCLUSIONS
  • ACKNOWLEDGMENTS
  • REFERENCES
• 5. Reactive and Cognitive Search Strategies for Olfactory Robots
  Dominique Martinez and Eduardo Martin Moraud.
  • 5.1 INTRODUCTION
  • 5.2 REACTIVE AND COGNITIVE SEARCH STRATEGIES: SPIRAL-SURGE AND INFOTAXIS
  • 5.3 NUMERICAL COMPARISONS BETWEEN SPIRAL-SURGE AND INFOTAXIS
  • 5.4 ROBOTIC IMPLEMENTATION OF INFOTAXIS
  • 5.5 PERSPECTIVES AND CONCLUSION
  • 5.6 APPENDIX: SOURCE CODE FOR INFOTAXIS AND SPIRAL-SURGE
  • REFERENCES
• 6. Performance of a Computational Model of the Mammalian Olfactory System
  Simon Benjaminsson, Pawel Herman, and Anders Lansner.
  • 6.1 INTRODUCTION
  • 6.2 DATA DESCRIPTION
  • 6.3 RESULTS ON SYNTHETIC DATA
  • 6.4 RESULTS ON POLYMER SENSOR DATA
  • 6.5 DISCUSSION
  • 6.6 SUMMARY AND CONCLUSIONS
  • ACKNOWLEDGMENTS
  • REFERENCES