2009
|
Raman, B; Gutierrez-Osuna, R Relating sensor responses of odorants to their organoleptic properties by means of a biologically-inspired model of receptor neuron convergence onto olfactory bulb Book Chapter In: Biologically inspired signal processing for chemical sensing, pp. 93–108, Springer Berlin/Heidelberg, 2009. @inbook{raman2009relating,
title = {Relating sensor responses of odorants to their organoleptic properties by means of a biologically-inspired model of receptor neuron convergence onto olfactory bulb},
author = {B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2009relating.pdf},
year = {2009},
date = {2009-01-01},
booktitle = {Biologically inspired signal processing for chemical sensing},
pages = {93--108},
publisher = {Springer Berlin/Heidelberg},
abstract = {This volume presents a collection of research advances in biologically inspired signal processing for chemical sensing. The olfactory system, and the gustatory system to a minor extent, has been taken in the last decades as a source of inspiration to develop artificial sensing systems. The performance of this biological system outperforms in many aspects that of their artificial counterpart. Thus, the goal of researchers in this field is to understand and capture those features that make the olfactory system especially suited for the processing of chemical information. The recognition of odors by the olfactory system entails a number of signal processing functions such as preprocessing, dimensionality reduction, contrast enhancement, and classification. Using mathematical models to mimic the architecture of the olfactory system, these processing functions can be applied to chemical sensor signals. This book provides some background on the olfactory system including a review on information processing in the insect olfactory system along with a proposed signal processing architecture based on the mammalian cortex. It also provides some bio-inspired approaches to process chemical sensor signals such as an olfactory mucosa to improve odor separation and a model of olfactory receptor neuron convergence to correlate sensor responses to an odor and his organoleptic properties.},
keywords = {Neuromorphic models},
pubstate = {published},
tppubtype = {inbook}
}
This volume presents a collection of research advances in biologically inspired signal processing for chemical sensing. The olfactory system, and the gustatory system to a minor extent, has been taken in the last decades as a source of inspiration to develop artificial sensing systems. The performance of this biological system outperforms in many aspects that of their artificial counterpart. Thus, the goal of researchers in this field is to understand and capture those features that make the olfactory system especially suited for the processing of chemical information. The recognition of odors by the olfactory system entails a number of signal processing functions such as preprocessing, dimensionality reduction, contrast enhancement, and classification. Using mathematical models to mimic the architecture of the olfactory system, these processing functions can be applied to chemical sensor signals. This book provides some background on the olfactory system including a review on information processing in the insect olfactory system along with a proposed signal processing architecture based on the mammalian cortex. It also provides some bio-inspired approaches to process chemical sensor signals such as an olfactory mucosa to improve odor separation and a model of olfactory receptor neuron convergence to correlate sensor responses to an odor and his organoleptic properties. |
2007
|
Raman, B; Kotseroglou, T; Clark, L; Lebl, M; Gutierrez-Osuna, R Neuromorphic processing for optical microbead arrays: dimensionality reduction and contrast enhancement Journal Article In: Sensors Journal, IEEE, vol. 7, no. 4, pp. 506–514, 2007. @article{raman2007neuromorphic,
title = {Neuromorphic processing for optical microbead arrays: dimensionality reduction and contrast enhancement},
author = {B Raman and T Kotseroglou and L Clark and M Lebl and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2007neuromorphic.pdf},
year = {2007},
date = {2007-01-01},
journal = {Sensors Journal, IEEE},
volume = {7},
number = {4},
pages = {506--514},
publisher = {IEEE},
abstract = {This paper presents a neuromorphic approach for sensor-based machine olfaction that combines a portable chemical detection system based on microbead array technology with a biologically inspired model of signal processing in the olfactory bulb. The sensor array contains hundreds of microbeads coated with solvatochromic dyes adsorbed in, or covalently attached on, the matrix of various microspheres. When exposed to odors, each bead sensor responds with corresponding intensity changes, spectral shifts, and time-dependent variations associated with the fluorescent sensors. The bead array responses are subsequently processed using a model of olfactory circuits that capture the following two functions: chemotopic convergence of receptor neurons and center on-off surround lateral interactions. The first circuit performs dimensionality reduction, transforming the high-dimensional microbead array response into an organized spatial pattern (i.e., an odor image). The second circuit enhances the contrast of these spatial patterns, improving the separability of odors. The model is validated on an experimental dataset containing the responses of a large array of microbead sensors to five different analytes. Our results indicate that the model is able to significantly improve the separability between odor patterns, compared to that available from the raw sensor response.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This paper presents a neuromorphic approach for sensor-based machine olfaction that combines a portable chemical detection system based on microbead array technology with a biologically inspired model of signal processing in the olfactory bulb. The sensor array contains hundreds of microbeads coated with solvatochromic dyes adsorbed in, or covalently attached on, the matrix of various microspheres. When exposed to odors, each bead sensor responds with corresponding intensity changes, spectral shifts, and time-dependent variations associated with the fluorescent sensors. The bead array responses are subsequently processed using a model of olfactory circuits that capture the following two functions: chemotopic convergence of receptor neurons and center on-off surround lateral interactions. The first circuit performs dimensionality reduction, transforming the high-dimensional microbead array response into an organized spatial pattern (i.e., an odor image). The second circuit enhances the contrast of these spatial patterns, improving the separability of odors. The model is validated on an experimental dataset containing the responses of a large array of microbead sensors to five different analytes. Our results indicate that the model is able to significantly improve the separability between odor patterns, compared to that available from the raw sensor response. |
2006
|
Raman, B; Yamanaka, T; Gutierrez-Osuna, R Contrast enhancement of gas sensor array patterns with a neurodynamics model of the olfactory bulb Journal Article In: Sensors and Actuators B: Chemical, vol. 119, no. 2, pp. 547–555, 2006. @article{raman2006contrast,
title = {Contrast enhancement of gas sensor array patterns with a neurodynamics model of the olfactory bulb},
author = {B Raman and T Yamanaka and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2006contrast.pdf},
year = {2006},
date = {2006-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {119},
number = {2},
pages = {547--555},
publisher = {Elsevier},
abstract = {We propose a biologically inspired signal processing model capable of enhancing the discrimination of multivariate patterns from gassensorarrays. The model captures two functions in the early olfactorypathway: chemotopic convergence of sensory neurons onto the olfactorybulb, 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 initialcontrast 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.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
We propose a biologically inspired signal processing model capable of enhancing the discrimination of multivariate patterns from gassensorarrays. The model captures two functions in the early olfactorypathway: chemotopic convergence of sensory neurons onto the olfactorybulb, 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 initialcontrast 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. |
Gutierrez-Galvez, A; Gutierrez-Osuna, R Contrast enhancement and background suppression of chemosensor array patterns with the KIII model Journal Article In: International journal of intelligent systems, vol. 21, no. 9, pp. 937–953, 2006. @article{gutierrez2006contrast,
title = {Contrast enhancement and background suppression of chemosensor array patterns with the KIII model},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2006contrast.pdf},
year = {2006},
date = {2006-01-01},
journal = {International journal of intelligent systems},
volume = {21},
number = {9},
pages = {937--953},
publisher = {Wiley Online Library},
abstract = {Inspired by the ability of the olfactory bulb to enhance the contrast between odor representations, we propose a new hebbian learning rule that is able to increase the separability of odor patterns from gas sensor arrays. The proposed learning rule employs a hebbian term to build associations within odors and an anti-hebbian term to reduce correlated activity across odors. In addition to increasing the separability of patterns, the new learning rule can also achieve odor background suppression when combined with a habituation term. These two functions are demonstrated on Freeman's KIII, a neurodynamics model of the olfactory system. The system is first characterized on synthetic data, and also validated on experimental data from an array of chemical sensors exposed to organic solvents.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
Inspired by the ability of the olfactory bulb to enhance the contrast between odor representations, we propose a new hebbian learning rule that is able to increase the separability of odor patterns from gas sensor arrays. The proposed learning rule employs a hebbian term to build associations within odors and an anti-hebbian term to reduce correlated activity across odors. In addition to increasing the separability of patterns, the new learning rule can also achieve odor background suppression when combined with a habituation term. These two functions are demonstrated on Freeman's KIII, a neurodynamics model of the olfactory system. The system is first characterized on synthetic data, and also validated on experimental data from an array of chemical sensors exposed to organic solvents. |
Gutierrez-Galvez, A; Gutierrez-Osuna, R Increasing the separability of chemosensor array patterns with Hebbian/anti-Hebbian learning Journal Article In: Sensors and Actuators B: Chemical, vol. 116, no. 1, pp. 29–35, 2006. @article{gutierrez2006increasing,
title = {Increasing the separability of chemosensor array patterns with Hebbian/anti-Hebbian learning},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2006increasing.pdf},
year = {2006},
date = {2006-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {116},
number = {1},
pages = {29--35},
publisher = {Elsevier},
abstract = {The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a newHebbian/anti-Hebbianlearning rule to increase the separability of sensor-arraypatterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The KIII model with the new learning rule is characterized on synthetic data and validated on experimental data from an array of temperature-modulated metal-oxide sensors. Our results show that the performance of the model is comparable to that obtained with Linear Discriminant Analysis (LDA). Furthermore, the model is able to increase patternseparability for different concentrations of three odorants: allyl-alcohol, tert-butanol, and benzene, even though it is only trained with the gas sensor response to the highest concentration.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a newHebbian/anti-Hebbianlearning rule to increase the separability of sensor-arraypatterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The KIII model with the new learning rule is characterized on synthetic data and validated on experimental data from an array of temperature-modulated metal-oxide sensors. Our results show that the performance of the model is comparable to that obtained with Linear Discriminant Analysis (LDA). Furthermore, the model is able to increase patternseparability for different concentrations of three odorants: allyl-alcohol, tert-butanol, and benzene, even though it is only trained with the gas sensor response to the highest concentration. |
Raman, B; Gutierrez-Osuna, R Concentration normalization with a model of gain control in the olfactory bulb Journal Article In: Sensors and Actuators B: Chemical, vol. 116, no. 1, pp. 36–42, 2006. @article{raman2006concentration,
title = {Concentration normalization with a model of gain control in the olfactory bulb},
author = {B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2006concentration.pdf},
year = {2006},
date = {2006-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {116},
number = {1},
pages = {36--42},
publisher = {Elsevier},
abstract = {This article presents a biologically inspired model capable of removing concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in theolfactorybulb, which is mediated by periglomerular interneurons. To simulate inputs to the olfactorybulb, signals from a chemosensor array are first processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odor-specific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells in the olfactorybulb, is used to compress concentration information. The model is validated using experimental data from an array of temperature-modulated metal-oxide chemoresistors.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This article presents a biologically inspired model capable of removing concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in theolfactorybulb, which is mediated by periglomerular interneurons. To simulate inputs to the olfactorybulb, signals from a chemosensor array are first processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odor-specific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells in the olfactorybulb, is used to compress concentration information. The model is validated using experimental data from an array of temperature-modulated metal-oxide chemoresistors. |
Perera-Lluna, A; Yamanaka, T; Gutierrez-Galvez, A; Raman, B; Gutierrez-Osuna, R A dimensionality-reduction technique inspired by receptor convergence in the olfactory system Journal Article In: Sensors and Actuators B: Chemical, vol. 116, no. 1, pp. 17–22, 2006. @article{perera2006dimensionality,
title = {A dimensionality-reduction technique inspired by receptor convergence in the olfactory system},
author = {A Perera-Lluna and T Yamanaka and A Gutierrez-Galvez and B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/perera2006dimensionality.pdf},
year = {2006},
date = {2006-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {116},
number = {1},
pages = {17--22},
publisher = {Elsevier},
abstract = {In this paper, we propose a new technique for feature extraction/selection based on the projection of sensor features in class space while taking into account the sensor variance. The proposed technique is inspired by the organization of the early stages in the biological olfactory system. The algorithm proves to be highly suitable for high-dimensional feature vectors. The performance shows robustness with problems where only a small number of samples are available as a training dataset. We demonstrate the method on experimental data from two metal oxide sensors driven by a sinusoidal temperature profile.},
keywords = {Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a new technique for feature extraction/selection based on the projection of sensor features in class space while taking into account the sensor variance. The proposed technique is inspired by the organization of the early stages in the biological olfactory system. The algorithm proves to be highly suitable for high-dimensional feature vectors. The performance shows robustness with problems where only a small number of samples are available as a training dataset. We demonstrate the method on experimental data from two metal oxide sensors driven by a sinusoidal temperature profile. |
Raman, B; Sun, P A; Gutierrez-Galvez, A; Gutierrez-Osuna, R Processing of chemical sensor arrays with a biologically inspired model of olfactory coding Journal Article In: Neural Networks, IEEE Transactions on, vol. 17, no. 4, pp. 1015–1024, 2006. @article{raman2006processing,
title = {Processing of chemical sensor arrays with a biologically inspired model of olfactory coding},
author = {B Raman and P A Sun and A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2006processing.pdf},
year = {2006},
date = {2006-01-01},
journal = {Neural Networks, IEEE Transactions on},
volume = {17},
number = {4},
pages = {1015--1024},
publisher = {IEEE},
abstract = {This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology. |
2005
|
Raman, B; Gutierrez-Osuna, R Concentration normalization with a model of gain control in the olfactory bulb Journal Article In: Sensors and Actuators B: Chemical, vol. 116, no. 1, pp. 36-42, 2005. @article{raman2005sensors,
title = {Concentration normalization with a model of gain control in the olfactory bulb},
author = {B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2005sensors.pdf},
year = {2005},
date = {2005-04-13},
booktitle = {Proceedings of the 11th International Symposium on Olfaction and Electronic Nose},
journal = {Sensors and Actuators B: Chemical},
volume = {116},
number = {1},
pages = {36-42},
abstract = {This article presents a biologically-inspired model to remove concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in the olfactory bulb, mediated by periglomerular interneurons. To simulate inputs to the olfactory bulb, sensor-array data are processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odorspecific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells, compresses the concentration information. The model is validated using experimental data from an array of temperature-modulated metaloxide sensors.},
keywords = {Chemical sensors, Neuromorphic models, Temperature modulation},
pubstate = {published},
tppubtype = {article}
}
This article presents a biologically-inspired model to remove concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in the olfactory bulb, mediated by periglomerular interneurons. To simulate inputs to the olfactory bulb, sensor-array data are processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odorspecific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells, compresses the concentration information. The model is validated using experimental data from an array of temperature-modulated metaloxide sensors. |
Perera-Lluna, A; Yamanaka, T; Gutierrez-Gálvez, A; Raman, B; Gutierrez-Osuna, R A dimensionality-reduction technique inspired by receptor convergence in the olfactory system Conference Proceedings of the 11th International Symposium on Olfaction and Electronic Nose, 2005. @conference{perera2005isoen,
title = {A dimensionality-reduction technique inspired by receptor convergence in the olfactory system},
author = {A Perera-Lluna and T Yamanaka and A Gutierrez-Gálvez and B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/perera2005isoen.pdf},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of the 11th International Symposium on Olfaction and Electronic Nose},
abstract = {In this paper we propose a new technique for feature extraction/selection based on the projection of sensor features in class space and taking into account the sensor variance. The proposed technique is inspired by the organization of the early stages in the biological olfactory system, and proves to be highly suitable for high-dimensional feature vectors with small number of training samples. We demonstrate the method on experimental data from two metal oxide sensors driven by a sinusoidal temperature profile.},
keywords = {Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
In this paper we propose a new technique for feature extraction/selection based on the projection of sensor features in class space and taking into account the sensor variance. The proposed technique is inspired by the organization of the early stages in the biological olfactory system, and proves to be highly suitable for high-dimensional feature vectors with small number of training samples. We demonstrate the method on experimental data from two metal oxide sensors driven by a sinusoidal temperature profile. |
Gutierrez-Galvez, A; Gutierrez-Osuna, R Contrast enhancement of sensor-array patterns through hebbian/antihebbian learning Conference Proceedings of the 11th International Symposium on Olfaction and Electronic Nose, 2005. @conference{gutierrez2005contrast,
title = {Contrast enhancement of sensor-array patterns through hebbian/antihebbian learning},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2005isoen.pdf},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of the 11th International Symposium on Olfaction and Electronic Nose},
abstract = {The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a new Hebbian/anti-Hebbian learning rule to increase the contrast of sensor-array patterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The system is characterized on synthetic data showing its ability to increase the separation between patterns and its robustness against noise. Experimental data from an array of temperature-modulated metal-oxide sensors is used to validate the contrast enhancement ability of the system.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a new Hebbian/anti-Hebbian learning rule to increase the contrast of sensor-array patterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The system is characterized on synthetic data showing its ability to increase the separation between patterns and its robustness against noise. Experimental data from an array of temperature-modulated metal-oxide sensors is used to validate the contrast enhancement ability of the system. |
Raman, B; Gutierrez-Osuna, R Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction Conference Proceedings of IEEE International Joint Conference on Neural Networks, IEEE 2005. @conference{raman2005ijcnn,
title = {Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction},
author = {B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2005ijcnn.pdf},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of IEEE International Joint Conference on Neural Networks},
pages = {131--136},
organization = {IEEE},
abstract = {We present a model of olfactory bulb-cortex interaction for the purpose of mixture processing with gas sensor arrays. The olfactory bulb is modeled with a neurodynamic model whose lateral inhibitory connections are learned through a modified Hebbian-anti-Hebbian rule. Bulbar outputs are then projected in a non-topographic fashion onto the olfactory cortex. Associational connections within cortex using Hebbian learning form a content addressable memory. Finally, inhibitory feedback from cortex is used to modulate bulbar activity. Depending on the form of feedback, Hebbian or anti-Hebbian, the model is able to perform background suppression or mixture segmentation. The model is validated on experimental data from a gas sensor array.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
We present a model of olfactory bulb-cortex interaction for the purpose of mixture processing with gas sensor arrays. The olfactory bulb is modeled with a neurodynamic model whose lateral inhibitory connections are learned through a modified Hebbian-anti-Hebbian rule. Bulbar outputs are then projected in a non-topographic fashion onto the olfactory cortex. Associational connections within cortex using Hebbian learning form a content addressable memory. Finally, inhibitory feedback from cortex is used to modulate bulbar activity. Depending on the form of feedback, Hebbian or anti-Hebbian, the model is able to perform background suppression or mixture segmentation. The model is validated on experimental data from a gas sensor array. |
2004
|
Gutierrez-Galvez, A; Gutierrez-Osuna, R; Raman, B Pattern recognition for chemosensor arrays with the KIII model Conference Proceedings of the 2004 Symposium on Intentional Dynamic Systems, 2004. @conference{gutierrez2004pattern,
title = {Pattern recognition for chemosensor arrays with the KIII model},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna and B Raman},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2004pattern.pdf},
year = {2004},
date = {2004-04-24},
booktitle = {Proceedings of the 2004 Symposium on Intentional Dynamic Systems},
journal = {Proc. 2004 Symposium on Intentional Dynamic Systems},
pages = {24--26},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
|
Raman, B; Gutierrez-Galvez, A; Perera-Lluna, A; Gutierrez-Osuna, R Sensor-based machine olfaction with a neurodynamics model of the olfactory bulb Conference Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE 2004. @conference{raman2004sensor,
title = {Sensor-based machine olfaction with a neurodynamics model of the olfactory bulb},
author = {B Raman and A Gutierrez-Galvez and A Perera-Lluna and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2004sensor.pdf},
year = {2004},
date = {2004-01-01},
booktitle = {Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages = {319--324},
organization = {IEEE},
abstract = {We propose a biologically inspired model of olfactory processing for chemosensor arrays. The model captures three functions in the early olfactory pathway: chemotopic convergence of receptor neurons onto the olfactory bulb, center on-off surround lateral interactions, and adaptation to sustained stimuli. The projection of ORNs onto glomerular units is simulated with a self-organizing model of chemotopic convergence, which leads to odor specific spatial patterning. This information serves as an input 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. Finally, slow adaptation of mitral cells adds a temporal dimension to the spatial patterns that further enhances odor discrimination. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors.},
keywords = {Machine olfaction, Metal-oxide sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
We propose a biologically inspired model of olfactory processing for chemosensor arrays. The model captures three functions in the early olfactory pathway: chemotopic convergence of receptor neurons onto the olfactory bulb, center on-off surround lateral interactions, and adaptation to sustained stimuli. The projection of ORNs onto glomerular units is simulated with a self-organizing model of chemotopic convergence, which leads to odor specific spatial patterning. This information serves as an input 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. Finally, slow adaptation of mitral cells adds a temporal dimension to the spatial patterns that further enhances odor discrimination. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors. |
Raman, B; Gutierrez-Osuna, R Chemosensory processing in a spiking model of the olfactory bulb: chemotopic convergence and center surround inhibition Conference NIPS, 2004. @conference{raman2004chemosensory,
title = {Chemosensory processing in a spiking model of the olfactory bulb: chemotopic convergence and center surround inhibition},
author = {B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2004chemosensory.pdf},
year = {2004},
date = {2004-01-01},
booktitle = {NIPS},
abstract = {This paper presents a neuromorphic model of two olfactory signalprocessing primitives: chemotopic convergence of olfactory receptor neurons, and center on-off surround lateral inhibition in the olfactory bulb. A self-organizing model of receptor convergence onto glomeruli is used to generate a spatially organized map, an olfactory image. This map serves as input to a lattice of spiking neurons with lateral connections. The dynamics of this recurrent network transforms the initial olfactory image into a spatio-temporal pattern that evolves and stabilizes into odor- and intensity-coding attractors. The model is validated using experimental data from an array of temperature-modulated gas sensors. Our results are consistent with recent neurobiological findings on the antennal lobe of the honeybee and the locust.},
keywords = {Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
This paper presents a neuromorphic model of two olfactory signalprocessing primitives: chemotopic convergence of olfactory receptor neurons, and center on-off surround lateral inhibition in the olfactory bulb. A self-organizing model of receptor convergence onto glomeruli is used to generate a spatially organized map, an olfactory image. This map serves as input to a lattice of spiking neurons with lateral connections. The dynamics of this recurrent network transforms the initial olfactory image into a spatio-temporal pattern that evolves and stabilizes into odor- and intensity-coding attractors. The model is validated using experimental data from an array of temperature-modulated gas sensors. Our results are consistent with recent neurobiological findings on the antennal lobe of the honeybee and the locust. |
2003
|
Gutierrez-Osuna, R; Gutierrez-Galvez, A Habituation in the KIII olfactory model with chemical sensor arrays Journal Article In: Neural Networks, IEEE Transactions on, vol. 14, no. 6, pp. 1565–1568, 2003. @article{gutierrez2003habituation,
title = {Habituation in the KIII olfactory model with chemical sensor arrays},
author = {R Gutierrez-Osuna and A Gutierrez-Galvez},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003habituation.pdf},
year = {2003},
date = {2003-01-01},
journal = {Neural Networks, IEEE Transactions on},
volume = {14},
number = {6},
pages = {1565--1568},
publisher = {IEEE},
abstract = {This paper presents a novel combination of chemical sensors and the KIII model for simulating mixture perception with a habituation process triggered by local activity. Stimuli are generated by partitioning feature space with labeled lines. Pattern completion is demonstrated through coherent oscillations across granule populations using experimental odor mixtures.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This paper presents a novel combination of chemical sensors and the KIII model for simulating mixture perception with a habituation process triggered by local activity. Stimuli are generated by partitioning feature space with labeled lines. Pattern completion is demonstrated through coherent oscillations across granule populations using experimental odor mixtures. |
Gutierrez-Galvez, A; Gutierrez-Osuna, R Pattern completion through phase coding in population neurodynamics Journal Article In: Neural networks, vol. 16, no. 5-6, pp. 649–656, 2003. @article{gutierrez2003pattern,
title = {Pattern completion through phase coding in population neurodynamics},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003pattern.pdf},
year = {2003},
date = {2003-01-01},
journal = {Neural networks},
volume = {16},
number = {5-6},
pages = {649--656},
publisher = {Elsevier},
abstract = {This article presents an alternative phasecoding mechanism for Freeman's KIII model of populationneurodynamics. Motivated by experimental evidence that supports the existence of a neural code based on synchronous oscillations, we propose an analogy between synchronization in neural populations and phase locking in KIII channels. An efficient method is proposed to extract phase differences across granule channels from their state-space trajectories. First, the scale invariance of the KIII model with respect to phase information is established. The phase code is then compared against the conventional amplitude code in terms of their bit-wise and across-fiber pattern recovery capabilities using decision-theoretic principles and a Hamming-distance classifier. Graph isomorphism in the Hebbian connections is exploited to perform an exhaustive evaluation of patterns on an 8-channel KIII model. Simulation results show that phase information outperforms amplitude information in the recovery of incomplete or corrupted stimuli.},
keywords = {Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This article presents an alternative phasecoding mechanism for Freeman's KIII model of populationneurodynamics. Motivated by experimental evidence that supports the existence of a neural code based on synchronous oscillations, we propose an analogy between synchronization in neural populations and phase locking in KIII channels. An efficient method is proposed to extract phase differences across granule channels from their state-space trajectories. First, the scale invariance of the KIII model with respect to phase information is established. The phase code is then compared against the conventional amplitude code in terms of their bit-wise and across-fiber pattern recovery capabilities using decision-theoretic principles and a Hamming-distance classifier. Graph isomorphism in the Hebbian connections is exploited to perform an exhaustive evaluation of patterns on an 8-channel KIII model. Simulation results show that phase information outperforms amplitude information in the recovery of incomplete or corrupted stimuli. |
Gutierrez-Galvez, A; Gutierrez-Osuna, R Coherent oscillations as a neural code in a model of the olfactory system Conference Proceedings of the International Joint Conference on Neural Networks, IEEE 2003. @conference{gutierrez2003coherent,
title = {Coherent oscillations as a neural code in a model of the olfactory system},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003coherent.pdf},
year = {2003},
date = {2003-01-01},
booktitle = {Proceedings of the International Joint Conference on Neural Networks},
pages = {341--346},
organization = {IEEE},
abstract = {This paper presents an investigation of two odor-coding mechanisms in Freeman's KIII neurodynamics model. Motivated by experimental evidence that supports the existence of a neural code based on synchronous oscillations, we propose an analogy between synchronization in neural populations and phase locking in KIII channels. The information carried by the phase is compared against the conventional amplitude code in terms of pattern-recovery capabilities. First, the scalar invariance of the KIII with respect to phase information is established. Symmetries and redundancies in the associative memory matrices are then exploited to perform an exhaustive evaluation of patterns on an 8-channel model. Simulation results show that phase information outperforms amplitude information in the recovery of odor patterns from incomplete or corrupted sensory stimulus.},
keywords = {Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
This paper presents an investigation of two odor-coding mechanisms in Freeman's KIII neurodynamics model. Motivated by experimental evidence that supports the existence of a neural code based on synchronous oscillations, we propose an analogy between synchronization in neural populations and phase locking in KIII channels. The information carried by the phase is compared against the conventional amplitude code in terms of pattern-recovery capabilities. First, the scalar invariance of the KIII with respect to phase information is established. Symmetries and redundancies in the associative memory matrices are then exploited to perform an exhaustive evaluation of patterns on an 8-channel model. Simulation results show that phase information outperforms amplitude information in the recovery of odor patterns from incomplete or corrupted sensory stimulus. |
Gutierrez-Osuna, R; Powar, N Odor mixtures and chemosensory adaptation in gas sensor arrays Journal Article In: International Journal on Artificial Intelligence Tools, vol. 12, no. 1, pp. 1–16, 2003. @article{gutierrez2003odor,
title = {Odor mixtures and chemosensory adaptation in gas sensor arrays},
author = {R Gutierrez-Osuna and N Powar},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003odor.pdf},
year = {2003},
date = {2003-01-01},
journal = {International Journal on Artificial Intelligence Tools},
volume = {12},
number = {1},
pages = {1--16},
publisher = {WORLD SCIENTIFIC PUBLISHING},
abstract = {Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.},
keywords = {Metal-oxide sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors. |
2002
|
Gutierrez-Osuna, R; Gutierrez-Galvez, A Habituation in the KIII olfactory model using gas sensor arrays Conference Proceedings of the 9th International Symposium on Olfaction and Electronic Nose, 2002. @conference{gutierrez2002habituation,
title = {Habituation in the KIII olfactory model using gas sensor arrays},
author = {R Gutierrez-Osuna and A Gutierrez-Galvez},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2002habituation.pdf},
year = {2002},
date = {2002-01-01},
booktitle = {Proceedings of the 9th International Symposium on Olfaction and Electronic Nose},
pages = {171--173},
abstract = {Inspired by the habituation process in the olfactory system, this article presents an approach for analyzing electronic-nose data using Freeman’s KIII neurodynamics model. In order to ensure the additivity of patterns from odor mixtures, input data from a gas sensor array is first processed with a family of discriminant functions that yield an orthogonal binary representation. The process of habituation is then simulated through synaptic depression with a decay term that reduces the strength of mitral and granule connections when the KIII model is excited with a continuous stimulus. As a result, the system is able to mimic the effects of habituation when processing odor mixtures with gas sensor arrays.},
keywords = {Metal-oxide sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
Inspired by the habituation process in the olfactory system, this article presents an approach for analyzing electronic-nose data using Freeman’s KIII neurodynamics model. In order to ensure the additivity of patterns from odor mixtures, input data from a gas sensor array is first processed with a family of discriminant functions that yield an orthogonal binary representation. The process of habituation is then simulated through synaptic depression with a decay term that reduces the strength of mitral and granule connections when the KIII model is excited with a continuous stimulus. As a result, the system is able to mimic the effects of habituation when processing odor mixtures with gas sensor arrays. |
Gutierrez-Osuna, R; Sun, P A biologically-plausible computational architecture for sensor-based machine olfaction Conference Proceedings of the ninth International Symposium on Olfaction and Electronic Nose, 2002. @conference{gutierrez2002biologically,
title = {A biologically-plausible computational architecture for sensor-based machine olfaction},
author = {R Gutierrez-Osuna and P Sun},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2002biologically.pdf},
year = {2002},
date = {2002-01-01},
booktitle = {Proceedings of the ninth International Symposium on Olfaction and Electronic Nose},
pages = {57--59},
abstract = {This article presents a computational architecture for sensor-based machine olfaction based on biologically plausible models of the early stages in the olfactory pathway. We derive a concentration-response model that maps conventional sensor-array patterns into activation patterns for a population of olfactory receptor neurons (ORNs). A chemotopic convergence model is employed to generate spatial activation patterns at the glomerular layer that are consistent with neurobiology. These glomerular images serve as inputs to an implementation of Freeman’s KIII neurodynamics model.},
keywords = {Machine olfaction, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
This article presents a computational architecture for sensor-based machine olfaction based on biologically plausible models of the early stages in the olfactory pathway. We derive a concentration-response model that maps conventional sensor-array patterns into activation patterns for a population of olfactory receptor neurons (ORNs). A chemotopic convergence model is employed to generate spatial activation patterns at the glomerular layer that are consistent with neurobiology. These glomerular images serve as inputs to an implementation of Freeman’s KIII neurodynamics model. |
Gutierrez-Osuna, R A self-organizing model of chemotopic convergence for olfactory coding Conference Proceedings of the 2nd Joint EMBS-BMES Conference, vol. 1, IEEE 2002. @conference{gutierrez2002self,
title = {A self-organizing model of chemotopic convergence for olfactory coding},
author = {R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2002self.pdf},
year = {2002},
date = {2002-01-01},
booktitle = {Proceedings of the 2nd Joint EMBS-BMES Conference},
volume = {1},
pages = {236--237},
organization = {IEEE},
abstract = {Presents a self-organizing model of convergence for the early stages of the olfactory pathway. The model generates a chemotopic projection from olfactory receptor neurons onto glomeruli based on receptor affinity distributions. The resulting glomerular images reveal an olfactory code consistent with neurobiology, whereby odor quality is encoded by a unique spatial pattern across glomeruli, and odor concentration is related to the intensity and spread of this pattern. The model is also able to predict a broadening of the intensity tuning range of glomeruli.},
keywords = {Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
Presents a self-organizing model of convergence for the early stages of the olfactory pathway. The model generates a chemotopic projection from olfactory receptor neurons onto glomeruli based on receptor affinity distributions. The resulting glomerular images reveal an olfactory code consistent with neurobiology, whereby odor quality is encoded by a unique spatial pattern across glomeruli, and odor concentration is related to the intensity and spread of this pattern. The model is also able to predict a broadening of the intensity tuning range of glomeruli. |
2001
|
Gutierrez-Osuna, R; Powar, N; Sun, P Chemosensory adaptation in an electronic nose Conference Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering Conference, IEEE 2001. @conference{gutierrez2001chemosensory,
title = {Chemosensory adaptation in an electronic nose},
author = {R Gutierrez-Osuna and N Powar and P Sun},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2001chemosensory.pdf},
year = {2001},
date = {2001-01-01},
booktitle = {Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering Conference},
pages = {223--229},
organization = {IEEE},
abstract = {This article presents a computational mechanism inspired by the process of chemosensory adaptation in the mammalian olfactory system. The algorithm operates on multiple subsets of the sensory space, generating a family of discriminant functions for different volatile compounds. A set of selectivity coefficients is associated to each discriminant function on the basis of its behavior in the presence of mixtures. These coefficients are employed to form a weighted average of the discriminant functions and establish a feedback signal that reduces the contribution of certain sensory inputs, inhibiting the overall selectivity of the system to previously detected analytes. The algorithm is validated on a database of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.},
keywords = {Chemical sensors, Electronic nose, Metal-oxide sensors, Neuromorphic models, Temperature modulation},
pubstate = {published},
tppubtype = {conference}
}
This article presents a computational mechanism inspired by the process of chemosensory adaptation in the mammalian olfactory system. The algorithm operates on multiple subsets of the sensory space, generating a family of discriminant functions for different volatile compounds. A set of selectivity coefficients is associated to each discriminant function on the basis of its behavior in the presence of mixtures. These coefficients are employed to form a weighted average of the discriminant functions and establish a feedback signal that reduces the contribution of certain sensory inputs, inhibiting the overall selectivity of the system to previously detected analytes. The algorithm is validated on a database of organic solvents using an array of temperature-modulated metal-oxide chemoresistors. |