The Experts below are selected from a list of 195 Experts worldwide ranked by ideXlab platform
Xiaojing Huang - One of the best experts on this subject based on the ideXlab platform.
-
Performance of Impulse-Train-Modulated Ultra-Wideband Systems
IEEE Transactions on Communications, 2006Co-Authors: Xiaojing HuangAbstract:The performance of Impulse-Train-modulated ultra-wideband (UWB) systems for the ideal additive white Gaussian noise channel is analyzed in this letter. The derived formulae are also used to optimize the modulation parameter of a Gaussian monocycle UWB Impulse radio
-
performances of Impulse Train modulated ultra wideband systems
International Conference on Communications, 2002Co-Authors: Xiaojing HuangAbstract:This paper analyzes the performances of three Impulse Train modulated ultra-wideband (UWB) communications systems in an additive white Gaussian noise (AWGN) channel. First, the mathematical models for describing biphase, pulse position and hybrid modulated ultra-wideband signals are developed and the decision rules for detecting them with only AWGN interference are proposed. Then, the exact formulae of the bit error probabilities of these UWB systems and their closed-form approximations are derived. Finally, the derived formulae are applied to optimize the modulation parameter of a Gaussian monocycle UWB Impulse radio.
-
ICC - Performances of Impulse Train modulated ultra-wideband systems
2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333), 1Co-Authors: Xiaojing HuangAbstract:This paper analyzes the performances of three Impulse Train modulated ultra-wideband (UWB) communications systems in an additive white Gaussian noise (AWGN) channel. First, the mathematical models for describing biphase, pulse position and hybrid modulated ultra-wideband signals are developed and the decision rules for detecting them with only AWGN interference are proposed. Then, the exact formulae of the bit error probabilities of these UWB systems and their closed-form approximations are derived. Finally, the derived formulae are applied to optimize the modulation parameter of a Gaussian monocycle UWB Impulse radio.
Theodore W. Berger - One of the best experts on this subject based on the ideXlab platform.
-
Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation
Annals of biomedical engineering, 2007Co-Authors: Ghassan Gholmieh, Spiros Courellis, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:A comprehensive, quantitative description of the nonlinear dynamic characteristics of the short-term plasticity (STP) in the CA1 hippocampal region is presented. It is based on the Volterra–Poisson modeling approach using random Impulse Train (RIT) stimuli. In vitro hippocampal slice preparations were used from adult rats. RIT stimuli were applied at the Schaffer collaterals and population spike responses were recorded at the CA1 cell body layer. The computed STP descriptors that capture the nonlinear dynamics of the underlying STP mechanisms were the Volterra–Poisson kernels. The kernels quantified the presence of facilitatory and inhibitory STP behavior in magnitude and duration. A third order Volterra–Poisson STP model was introduced that accurately predicted in-sample and out-of-sample system responses. The proposed model could also accurately predict Impulse pair and short Impulse Train system responses.
-
An efficient method for studying short-term plasticity with random Impulse Train stimuli.
Journal of neuroscience methods, 2002Co-Authors: Ghassan Gholmieh, Spiros Courellis, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:Abstract In this article, we introduce an efficient method that models quantitatively nonlinear dynamics associated with short-term plasticity (STP) in biological neural systems. It is based on the Voterra–Wiener modeling approach adapted for special stimulus/response datasets. The stimuli are random Impulse Trains (RITs) of fixed amplitude and Poisson distributed, variable interImpulse intervals. The class of stimuli, we use can be viewed as a hybrid between the paired Impulse approach (variable interImpulse interval between two input Impulses) and the fixed frequency approach (Impulses repeated at fixed intervals, varying in frequency from one stimulus dataset to the next). The responses are sequences of population spike amplitudes of variable size and are assumed to be contemporaneous with the corresponding Impulses in the RITs they are evoked by. The nonlinear dynamics of the mechanisms underlying STP are captured by kernels used to create compact STP models with predictive capabilities. Compared to similar methods in the literature, the method presented in this article provides a comprehensive model of STP with considerable improvement in prediction accuracy and requires shorter experimental data collection time.
Ghassan Gholmieh - One of the best experts on this subject based on the ideXlab platform.
-
Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation
Annals of biomedical engineering, 2007Co-Authors: Ghassan Gholmieh, Spiros Courellis, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:A comprehensive, quantitative description of the nonlinear dynamic characteristics of the short-term plasticity (STP) in the CA1 hippocampal region is presented. It is based on the Volterra–Poisson modeling approach using random Impulse Train (RIT) stimuli. In vitro hippocampal slice preparations were used from adult rats. RIT stimuli were applied at the Schaffer collaterals and population spike responses were recorded at the CA1 cell body layer. The computed STP descriptors that capture the nonlinear dynamics of the underlying STP mechanisms were the Volterra–Poisson kernels. The kernels quantified the presence of facilitatory and inhibitory STP behavior in magnitude and duration. A third order Volterra–Poisson STP model was introduced that accurately predicted in-sample and out-of-sample system responses. The proposed model could also accurately predict Impulse pair and short Impulse Train system responses.
-
An efficient method for studying short-term plasticity with random Impulse Train stimuli.
Journal of neuroscience methods, 2002Co-Authors: Ghassan Gholmieh, Spiros Courellis, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:Abstract In this article, we introduce an efficient method that models quantitatively nonlinear dynamics associated with short-term plasticity (STP) in biological neural systems. It is based on the Voterra–Wiener modeling approach adapted for special stimulus/response datasets. The stimuli are random Impulse Trains (RITs) of fixed amplitude and Poisson distributed, variable interImpulse intervals. The class of stimuli, we use can be viewed as a hybrid between the paired Impulse approach (variable interImpulse interval between two input Impulses) and the fixed frequency approach (Impulses repeated at fixed intervals, varying in frequency from one stimulus dataset to the next). The responses are sequences of population spike amplitudes of variable size and are assumed to be contemporaneous with the corresponding Impulses in the RITs they are evoked by. The nonlinear dynamics of the mechanisms underlying STP are captured by kernels used to create compact STP models with predictive capabilities. Compared to similar methods in the literature, the method presented in this article provides a comprehensive model of STP with considerable improvement in prediction accuracy and requires shorter experimental data collection time.
Vasilis Z. Marmarelis - One of the best experts on this subject based on the ideXlab platform.
-
Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation
Annals of biomedical engineering, 2007Co-Authors: Ghassan Gholmieh, Spiros Courellis, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:A comprehensive, quantitative description of the nonlinear dynamic characteristics of the short-term plasticity (STP) in the CA1 hippocampal region is presented. It is based on the Volterra–Poisson modeling approach using random Impulse Train (RIT) stimuli. In vitro hippocampal slice preparations were used from adult rats. RIT stimuli were applied at the Schaffer collaterals and population spike responses were recorded at the CA1 cell body layer. The computed STP descriptors that capture the nonlinear dynamics of the underlying STP mechanisms were the Volterra–Poisson kernels. The kernels quantified the presence of facilitatory and inhibitory STP behavior in magnitude and duration. A third order Volterra–Poisson STP model was introduced that accurately predicted in-sample and out-of-sample system responses. The proposed model could also accurately predict Impulse pair and short Impulse Train system responses.
-
An efficient method for studying short-term plasticity with random Impulse Train stimuli.
Journal of neuroscience methods, 2002Co-Authors: Ghassan Gholmieh, Spiros Courellis, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:Abstract In this article, we introduce an efficient method that models quantitatively nonlinear dynamics associated with short-term plasticity (STP) in biological neural systems. It is based on the Voterra–Wiener modeling approach adapted for special stimulus/response datasets. The stimuli are random Impulse Trains (RITs) of fixed amplitude and Poisson distributed, variable interImpulse intervals. The class of stimuli, we use can be viewed as a hybrid between the paired Impulse approach (variable interImpulse interval between two input Impulses) and the fixed frequency approach (Impulses repeated at fixed intervals, varying in frequency from one stimulus dataset to the next). The responses are sequences of population spike amplitudes of variable size and are assumed to be contemporaneous with the corresponding Impulses in the RITs they are evoked by. The nonlinear dynamics of the mechanisms underlying STP are captured by kernels used to create compact STP models with predictive capabilities. Compared to similar methods in the literature, the method presented in this article provides a comprehensive model of STP with considerable improvement in prediction accuracy and requires shorter experimental data collection time.
-
Identification of the lateral and medial perforant path of the hippocampus using single and dual random Impulse Train stimulation
Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 1Co-Authors: A. Dimoka, Spiros Courellis, Vasilis Z. Marmarelis, Dong Song, T.w. BergprAbstract:In this paper, we present a new method to characterize the nonlinearities resulting from the co-activity of two pathways that converge on a common postsynaptic element. For this purpose, we investigated the nonlinear dynamic relationship between the lateral and the medial perforant pathway of the hippocampal dentate gyrus, and the effects of these cross-pathway interactions on granule cell output. This method is employed to identify the two pathways based on differences in the kernels calculated using the Volterra-Poisson modeling approach. The kernels present pathway specific signatures as they capture the nonlinear dynamics of each pathway individually in the form of self-kernels, and the nonlinear dynamics of the interaction between the two pathways in the form of cross-kernels. We present preliminary results using data collected in-vitro from acute slices of adult rats via a mult-electrode array recording system. The stimuli were single-site and dual-site random Impulse Trains with Poisson distributed inter-Impulse intervals. The recorded responses from the granule cells were population spikes, simplified as discrete Impulses with variable amplitudes. The results of single-site and dual-site stimulation, reported in the paper, support the use of kernels as consistent path identification signatures across all inter-Impulse intervals within the memory of the biological system.
Spiros Courellis - One of the best experts on this subject based on the ideXlab platform.
-
Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation
Annals of biomedical engineering, 2007Co-Authors: Ghassan Gholmieh, Spiros Courellis, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:A comprehensive, quantitative description of the nonlinear dynamic characteristics of the short-term plasticity (STP) in the CA1 hippocampal region is presented. It is based on the Volterra–Poisson modeling approach using random Impulse Train (RIT) stimuli. In vitro hippocampal slice preparations were used from adult rats. RIT stimuli were applied at the Schaffer collaterals and population spike responses were recorded at the CA1 cell body layer. The computed STP descriptors that capture the nonlinear dynamics of the underlying STP mechanisms were the Volterra–Poisson kernels. The kernels quantified the presence of facilitatory and inhibitory STP behavior in magnitude and duration. A third order Volterra–Poisson STP model was introduced that accurately predicted in-sample and out-of-sample system responses. The proposed model could also accurately predict Impulse pair and short Impulse Train system responses.
-
An efficient method for studying short-term plasticity with random Impulse Train stimuli.
Journal of neuroscience methods, 2002Co-Authors: Ghassan Gholmieh, Spiros Courellis, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:Abstract In this article, we introduce an efficient method that models quantitatively nonlinear dynamics associated with short-term plasticity (STP) in biological neural systems. It is based on the Voterra–Wiener modeling approach adapted for special stimulus/response datasets. The stimuli are random Impulse Trains (RITs) of fixed amplitude and Poisson distributed, variable interImpulse intervals. The class of stimuli, we use can be viewed as a hybrid between the paired Impulse approach (variable interImpulse interval between two input Impulses) and the fixed frequency approach (Impulses repeated at fixed intervals, varying in frequency from one stimulus dataset to the next). The responses are sequences of population spike amplitudes of variable size and are assumed to be contemporaneous with the corresponding Impulses in the RITs they are evoked by. The nonlinear dynamics of the mechanisms underlying STP are captured by kernels used to create compact STP models with predictive capabilities. Compared to similar methods in the literature, the method presented in this article provides a comprehensive model of STP with considerable improvement in prediction accuracy and requires shorter experimental data collection time.
-
Identification of the lateral and medial perforant path of the hippocampus using single and dual random Impulse Train stimulation
Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 1Co-Authors: A. Dimoka, Spiros Courellis, Vasilis Z. Marmarelis, Dong Song, T.w. BergprAbstract:In this paper, we present a new method to characterize the nonlinearities resulting from the co-activity of two pathways that converge on a common postsynaptic element. For this purpose, we investigated the nonlinear dynamic relationship between the lateral and the medial perforant pathway of the hippocampal dentate gyrus, and the effects of these cross-pathway interactions on granule cell output. This method is employed to identify the two pathways based on differences in the kernels calculated using the Volterra-Poisson modeling approach. The kernels present pathway specific signatures as they capture the nonlinear dynamics of each pathway individually in the form of self-kernels, and the nonlinear dynamics of the interaction between the two pathways in the form of cross-kernels. We present preliminary results using data collected in-vitro from acute slices of adult rats via a mult-electrode array recording system. The stimuli were single-site and dual-site random Impulse Trains with Poisson distributed inter-Impulse intervals. The recorded responses from the granule cells were population spikes, simplified as discrete Impulses with variable amplitudes. The results of single-site and dual-site stimulation, reported in the paper, support the use of kernels as consistent path identification signatures across all inter-Impulse intervals within the memory of the biological system.
