A dynamic computational model of the parallel circuit on the basal ganglia-cortex associated with Parkinson's disease dementia
Biol Cybern. 2024 Apr 21. doi: 10.1007/s00422-024-00988-x. Online ahead of print.ABSTRACTThe cognitive impairment will gradually appear over time in Parkinson's patients, which is closely related to the basal ganglia-cortex network. This network contains two parallel circuits mediated by putamen and caudate nucleus, respectively. Based on the biophysical mean-field model, we construct a dynamic computational model of the parallel circuit in the basal ganglia-cortex network associated with Parkinson's disease dementia. The simulated results show that the decrease of power ratio in the prefrontal cortex is mainly caused by d...
Source: Biological Cybernetics - April 21, 2024 Category: Science Authors: Hao Yang XiaoLi Yang SiLu Yan Source Type: research
A dynamic computational model of the parallel circuit on the basal ganglia-cortex associated with Parkinson's disease dementia
Biol Cybern. 2024 Apr 21. doi: 10.1007/s00422-024-00988-x. Online ahead of print.ABSTRACTThe cognitive impairment will gradually appear over time in Parkinson's patients, which is closely related to the basal ganglia-cortex network. This network contains two parallel circuits mediated by putamen and caudate nucleus, respectively. Based on the biophysical mean-field model, we construct a dynamic computational model of the parallel circuit in the basal ganglia-cortex network associated with Parkinson's disease dementia. The simulated results show that the decrease of power ratio in the prefrontal cortex is mainly caused by d...
Source: Biological Cybernetics - April 21, 2024 Category: Science Authors: Hao Yang XiaoLi Yang SiLu Yan Source Type: research
A dynamic computational model of the parallel circuit on the basal ganglia-cortex associated with Parkinson's disease dementia
Biol Cybern. 2024 Apr 21. doi: 10.1007/s00422-024-00988-x. Online ahead of print.ABSTRACTThe cognitive impairment will gradually appear over time in Parkinson's patients, which is closely related to the basal ganglia-cortex network. This network contains two parallel circuits mediated by putamen and caudate nucleus, respectively. Based on the biophysical mean-field model, we construct a dynamic computational model of the parallel circuit in the basal ganglia-cortex network associated with Parkinson's disease dementia. The simulated results show that the decrease of power ratio in the prefrontal cortex is mainly caused by d...
Source: Biological Cybernetics - April 21, 2024 Category: Science Authors: Hao Yang XiaoLi Yang SiLu Yan Source Type: research
Controlling flat-foot limit cycle walkers with compliant joints based on local stability variation
This study investigates local stability of a four-link limit cycle walking biped with flat feet and compliant ankle joints. Local stability represents the behavior along the solution trajectory between Poincare sections, which can provide detailed information about the evolution of disturbances. The effects of ankle stiffness and foot structure on local stability are studied. In addition, we apply a control strategy based on local stability analysis to the limit cycle walker. Control is applied only in the phases with poor local stability. Simulation results show that the energy consumption is reduced without sacrificing d...
Source: Biological Cybernetics - April 19, 2024 Category: Science Authors: Yan Huang Yue Gao Qiang Huang Qining Wang Source Type: research
Empirical modeling and prediction of neuronal dynamics
We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the r...
Source: Biological Cybernetics - April 10, 2024 Category: Science Authors: Pau Fisco-Compte David Aquilu é-Llorens Nestor Roqueiro Enric Fossas Antoni Guillamon Source Type: research
Empirical modeling and prediction of neuronal dynamics
We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the r...
Source: Biological Cybernetics - April 10, 2024 Category: Science Authors: Pau Fisco-Compte David Aquilu é-Llorens Nestor Roqueiro Enric Fossas Antoni Guillamon Source Type: research
Empirical modeling and prediction of neuronal dynamics
We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the r...
Source: Biological Cybernetics - April 10, 2024 Category: Science Authors: Pau Fisco-Compte David Aquilu é-Llorens Nestor Roqueiro Enric Fossas Antoni Guillamon Source Type: research
Empirical modeling and prediction of neuronal dynamics
We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the r...
Source: Biological Cybernetics - April 10, 2024 Category: Science Authors: Pau Fisco-Compte David Aquilu é-Llorens Nestor Roqueiro Enric Fossas Antoni Guillamon Source Type: research
Empirical modeling and prediction of neuronal dynamics
We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the r...
Source: Biological Cybernetics - April 10, 2024 Category: Science Authors: Pau Fisco-Compte David Aquilu é-Llorens Nestor Roqueiro Enric Fossas Antoni Guillamon Source Type: research
Empirical modeling and prediction of neuronal dynamics
We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the r...
Source: Biological Cybernetics - April 10, 2024 Category: Science Authors: Pau Fisco-Compte David Aquilu é-Llorens Nestor Roqueiro Enric Fossas Antoni Guillamon Source Type: research
Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity
Biol Cybern. 2024 Apr 7. doi: 10.1007/s00422-024-00985-0. Online ahead of print.ABSTRACTStochastic models of synaptic plasticity must confront the corrosive influence of fluctuations in synaptic strength on patterns of synaptic connectivity. To solve this problem, we have proposed that synapses act as filters, integrating plasticity induction signals and expressing changes in synaptic strength only upon reaching filter threshold. Our earlier analytical study calculated the lifetimes of quasi-stable patterns of synaptic connectivity with synaptic filtering. We showed that the plasticity step size in a stochastic model of sp...
Source: Biological Cybernetics - April 7, 2024 Category: Science Authors: Terry Elliott Source Type: research
Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity
Biol Cybern. 2024 Apr 7. doi: 10.1007/s00422-024-00985-0. Online ahead of print.ABSTRACTStochastic models of synaptic plasticity must confront the corrosive influence of fluctuations in synaptic strength on patterns of synaptic connectivity. To solve this problem, we have proposed that synapses act as filters, integrating plasticity induction signals and expressing changes in synaptic strength only upon reaching filter threshold. Our earlier analytical study calculated the lifetimes of quasi-stable patterns of synaptic connectivity with synaptic filtering. We showed that the plasticity step size in a stochastic model of sp...
Source: Biological Cybernetics - April 7, 2024 Category: Science Authors: Terry Elliott Source Type: research
Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity
Biol Cybern. 2024 Apr 7. doi: 10.1007/s00422-024-00985-0. Online ahead of print.ABSTRACTStochastic models of synaptic plasticity must confront the corrosive influence of fluctuations in synaptic strength on patterns of synaptic connectivity. To solve this problem, we have proposed that synapses act as filters, integrating plasticity induction signals and expressing changes in synaptic strength only upon reaching filter threshold. Our earlier analytical study calculated the lifetimes of quasi-stable patterns of synaptic connectivity with synaptic filtering. We showed that the plasticity step size in a stochastic model of sp...
Source: Biological Cybernetics - April 7, 2024 Category: Science Authors: Terry Elliott Source Type: research
EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI
Biol Cybern. 2024 Mar 12. doi: 10.1007/s00422-024-00984-1. Online ahead of print.ABSTRACTMotor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational compl...
Source: Biological Cybernetics - March 13, 2024 Category: Science Authors: Yang Jiao Qian Zheng Dan Qiao Xun Lang Lei Xie Yi Pan Source Type: research
EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI
Biol Cybern. 2024 Mar 12. doi: 10.1007/s00422-024-00984-1. Online ahead of print.ABSTRACTMotor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational compl...
Source: Biological Cybernetics - March 13, 2024 Category: Science Authors: Yang Jiao Qian Zheng Dan Qiao Xun Lang Lei Xie Yi Pan Source Type: research
