@article {3510,
	title = {ARTIFICIAL NEURAL-NETWORK AND CLASSICAL LEAST-SQUARES METHODS FOR NEUROTRANSMITTER MIXTURE ANALYSIS},
	journal = {Journal of Neuroscience Methods},
	volume = {56},
	number = {2},
	year = {1995},
	note = {ISI Document Delivery No.: QH949Times Cited: 5Cited Reference Count: 28},
	month = {Feb},
	pages = {155-167},
	type = {Article},
	abstract = {Identification of individual components in biological mixtures can be a difficult problem regardless of the analytical method employed. In this work, Raman spectroscopy was chosen as a prototype analytical method due to its inherent versatility and applicability to aqueous media, making it useful for the study of biological samples. Artificial neural networks (ANNs) and the classical least-squares (CLS) method were used to identify and quantify the Raman spectra of the small-molecule neurotransmitters and mixtures of such molecules. The transfer functions used by a network, as well as the architecture of a network, played an important role in the ability of the network to identify the Raman spectra of individual neurotransmitters and the Raman spectra of neurotransmitter mixtures. Specifically, networks using sigmoid and hyperbolic tangent transfer functions generalized better from the mixtures in the training data set to those in the testing data sets than networks using sine functions. Networks with connections that permit the local processing of inputs generally performed better than other networks on all the testing data sets, and better than the CLS method of curve fitting, on novel spectra of some neurotransmitters. The CLS method was found to perform well on noisy, shifted, and difference spectra.},
	keywords = {ARTIFICIAL NEURAL, BACTERIA, CLASSICAL LEAST-SQUARES ANALYSIS, INVIVO VOLTAMMETRY, MIXTURE ANALYSIS, NETWORK, Raman spectroscopy, REGRESSION, SMALL-MOLECULE NEUROTRANSMITTER, SPECTRA, SYSTEM, UV RESONANCE RAMAN},
	isbn = {0165-0270},
	url = {<Go to ISI>://A1995QH94900006},
	author = {Schulze, H. G. and Greek, L. S. and Gorzalka, B. B. and Bree, A. V. and Blades, M. W. and Turner, R. F. B.}
}
@article {3510,
	title = {ARTIFICIAL NEURAL-NETWORK AND CLASSICAL LEAST-SQUARES METHODS FOR NEUROTRANSMITTER MIXTURE ANALYSIS},
	journal = {Journal of Neuroscience Methods},
	volume = {56},
	number = {2},
	year = {1995},
	note = {ISI Document Delivery No.: QH949Times Cited: 5Cited Reference Count: 28},
	month = {Feb},
	pages = {155-167},
	type = {Article},
	abstract = {Identification of individual components in biological mixtures can be a difficult problem regardless of the analytical method employed. In this work, Raman spectroscopy was chosen as a prototype analytical method due to its inherent versatility and applicability to aqueous media, making it useful for the study of biological samples. Artificial neural networks (ANNs) and the classical least-squares (CLS) method were used to identify and quantify the Raman spectra of the small-molecule neurotransmitters and mixtures of such molecules. The transfer functions used by a network, as well as the architecture of a network, played an important role in the ability of the network to identify the Raman spectra of individual neurotransmitters and the Raman spectra of neurotransmitter mixtures. Specifically, networks using sigmoid and hyperbolic tangent transfer functions generalized better from the mixtures in the training data set to those in the testing data sets than networks using sine functions. Networks with connections that permit the local processing of inputs generally performed better than other networks on all the testing data sets, and better than the CLS method of curve fitting, on novel spectra of some neurotransmitters. The CLS method was found to perform well on noisy, shifted, and difference spectra.},
	keywords = {ARTIFICIAL NEURAL, BACTERIA, CLASSICAL LEAST-SQUARES ANALYSIS, INVIVO VOLTAMMETRY, MIXTURE ANALYSIS, NETWORK, Raman spectroscopy, REGRESSION, SMALL-MOLECULE NEUROTRANSMITTER, SPECTRA, SYSTEM, UV RESONANCE RAMAN},
	isbn = {0165-0270},
	url = {<Go to ISI>://A1995QH94900006},
	author = {Schulze, H. G. and Greek, L. S. and Gorzalka, B. B. and Bree, A. V. and Blades, M. W. and Turner, R. F. B.}
}
@article {3161,
	title = {CHARACTERISTICS OF BACKPROPAGATION NEURAL NETWORKS EMPLOYED IN THE IDENTIFICATION OF NEUROTRANSMITTER RAMAN-SPECTRA},
	journal = {Applied Spectroscopy},
	volume = {48},
	number = {1},
	year = {1994},
	note = {ISI Document Delivery No.: MU524Times Cited: 18Cited Reference Count: 18},
	month = {Jan},
	pages = {50-57},
	type = {Article},
	abstract = {We have shown that neural networks are capable of accurately identifying the Raman spectra of aqueous solutions of small-molecule neurotransmitters. It was found that the networks performed optimally when the ratio of the number of hidden nodes to the number of input nodes was 0.16, that network accuracy increased with the number of input layer nodes, and that input features influenced the abilities of networks to discriminate or generalize between spectra. Furthermore, networks employing sine transfer functions for their hidden layers trained faster and were better at discriminating between closely related spectra, but they were less tolerant of spectral distortions than the networks using sigmoid transfer functions. The latter type of network produced superior results where generalization between spectra was required.},
	keywords = {artificial neural networks, BACKPROPAGATION NETWORKS, DISCRIMINATION, GENERALIZATION, NEUROTRANSMITTERS, RAMAN SPECTRA, SPECTROSCOPY, TRANSFER FUNCTION, UV RESONANCE RAMAN},
	isbn = {0003-7028},
	url = {<Go to ISI>://A1994MU52400011},
	author = {Schulze, H. G. and Blades, M. W. and Bree, A. V. and Gorzalka, B. B. and Greek, L. S. and Turner, R. F. B.}
}
@article {3161,
	title = {CHARACTERISTICS OF BACKPROPAGATION NEURAL NETWORKS EMPLOYED IN THE IDENTIFICATION OF NEUROTRANSMITTER RAMAN-SPECTRA},
	journal = {Applied Spectroscopy},
	volume = {48},
	number = {1},
	year = {1994},
	note = {ISI Document Delivery No.: MU524Times Cited: 18Cited Reference Count: 18},
	month = {Jan},
	pages = {50-57},
	type = {Article},
	abstract = {We have shown that neural networks are capable of accurately identifying the Raman spectra of aqueous solutions of small-molecule neurotransmitters. It was found that the networks performed optimally when the ratio of the number of hidden nodes to the number of input nodes was 0.16, that network accuracy increased with the number of input layer nodes, and that input features influenced the abilities of networks to discriminate or generalize between spectra. Furthermore, networks employing sine transfer functions for their hidden layers trained faster and were better at discriminating between closely related spectra, but they were less tolerant of spectral distortions than the networks using sigmoid transfer functions. The latter type of network produced superior results where generalization between spectra was required.},
	keywords = {artificial neural networks, BACKPROPAGATION NETWORKS, DISCRIMINATION, GENERALIZATION, NEUROTRANSMITTERS, RAMAN SPECTRA, SPECTROSCOPY, TRANSFER FUNCTION, UV RESONANCE RAMAN},
	isbn = {0003-7028},
	url = {<Go to ISI>://A1994MU52400011},
	author = {Schulze, H. G. and Blades, M. W. and Bree, A. V. and Gorzalka, B. B. and Greek, L. S. and Turner, R. F. B.}
}
