TY - JOUR
T1 - Contrast enhancement of gas sensor array patterns with a neurodynamics model of the olfactory bulb
AU - Raman, B.
AU - Yamanaka, T.
AU - Gutierrez-Osuna, R.
N1 - Funding Information:
Takao Yamanaka received the Bachelor of Engineering, the Master of Engineering, and the Ph.D. in Engineering from Tokyo Institute of Technology in 1996, 1998, and 2004, respectively. He was with Canon, Inc. from 1998 to 2000 in the area of image and signal processing. He is currently a postdoctoral fellow at Texas A&M University, supported by JSPS postdoctoral fellowship for research abroad. His research interests include neuromorphic engineering, intelligent sensors, machine learning, computational neuroscience, and machine olfaction.
Funding Information:
This material is based upon work supported by the National Science Foundation under CAREER award 9984426/0229598. T. Yamanaka is supported by a postdoctoral fellowship for research abroad (2004) from the Japan Society of the Promotion of Science.
PY - 2006/12/7
Y1 - 2006/12/7
N2 - We propose a biologically inspired signal processing model capable of enhancing the discrimination of multivariate patterns from gas sensor arrays. The model captures two functions in the early olfactory pathway: chemotopic convergence of sensory neurons onto the olfactory bulb, and center on-off surround lateral interactions. Sensor features are first topologically projected onto a two-dimensional lattice according to their selectivity profile, leading to odor-specific spatial patterning. The resulting patterns serve as inputs to a network of mitral cells with center on-off surround lateral inhibition, which enhances the initial contrast among odors and decouples odor identity from intensity. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors. Our results indicate that the model is able to improve the separability between odor patterns that is available at the inputs.
AB - We propose a biologically inspired signal processing model capable of enhancing the discrimination of multivariate patterns from gas sensor arrays. The model captures two functions in the early olfactory pathway: chemotopic convergence of sensory neurons onto the olfactory bulb, and center on-off surround lateral interactions. Sensor features are first topologically projected onto a two-dimensional lattice according to their selectivity profile, leading to odor-specific spatial patterning. The resulting patterns serve as inputs to a network of mitral cells with center on-off surround lateral inhibition, which enhances the initial contrast among odors and decouples odor identity from intensity. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors. Our results indicate that the model is able to improve the separability between odor patterns that is available at the inputs.
KW - Biologically inspired computation
KW - Contrast enhancement
KW - Electronic nose
KW - Gas sensors
KW - Lateral inhibition
UR - https://www.scopus.com/pages/publications/33748758708
U2 - 10.1016/j.snb.2006.01.035
DO - 10.1016/j.snb.2006.01.035
M3 - Article
AN - SCOPUS:33748758708
SN - 0925-4005
VL - 119
SP - 547
EP - 555
JO - Sensors and Actuators, B: Chemical
JF - Sensors and Actuators, B: Chemical
IS - 2
ER -