To further identify the dynamics of the period-adding bifurcation scenarios observed in both biological experiment and simulations with differential Chay model, this paper fits a discontinuous map of a slow control va...To further identify the dynamics of the period-adding bifurcation scenarios observed in both biological experiment and simulations with differential Chay model, this paper fits a discontinuous map of a slow control variable of Chay model based on simulation results. The procedure of period adding bifurcation scenario from period k to period k ＋ 1 bursting （k = 1, 2, 3, 4） involved in the period-adding cascades and the stochastic effect of noise near each bifurcation point is also reproduced in the discontinuous map. Moreover, dynamics of the border-collision bifurcation is identified in the discontinuous map, which is employed to understand the experimentally observed period increment sequence. The simple discontinuous map is of practical importance in modeling of collective behaviours of neural populations like synchronization in large neural circuits.展开更多
Neural firing patterns are investigated by using symbolic dynamics. Bifurcation behaviour of the Hindmarsh-Rose （HR） neuronal model is simulated with the external stimuli gradually decreasing, and various firing act...Neural firing patterns are investigated by using symbolic dynamics. Bifurcation behaviour of the Hindmarsh-Rose （HR） neuronal model is simulated with the external stimuli gradually decreasing, and various firing activities with different topological structures are orderly numbered. Through constructing first-return maps of interspike intervals, all firing patterns are described and identified by symbolic expressions. On the basis of ordering rules of symbolic sequences, the corresponding relation between parameters and firing patterns is established, which will be helpful for encoding neural information. Moreover, using the operation rule of ＊ product, generation mechanisms and intrinsic configurations of periodic patterns can be distinguished in detail. Results show that the symbolic approach is a powerful tool to study neural firing activities. In particular, such a coarse-grained way can be generalized in neural electropt/ysiological experiments to extract much valuable information from complicated experimental data.展开更多
Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that...Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that maps inputs directly to outputs, making it less difficult to optimize. In this paper, we incorporate differential information into the original residual block to improve the representative ability of the ResNet, allowing the modified network to capture more complex and metaphysical features. The proposed DFNet preserves the features after each convolutional operation in the residual block, and combines the feature maps of different levels of abstraction through the differential information. To verify the effectiveness of DFNet on image recognition, we select six distinct classification datasets. The experimental results show that our proposed DFNet has better performance and generalization ability than other state-of-the-art variants of ResNet in terms of classification accuracy and other statistical analysis.展开更多
The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics cause...The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.展开更多
Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new meth...Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.展开更多
This paper provides a perception that all things are connected. Starting with the perception of Metaphysics and how matter exists out of total void, complementary matter, or dark matter, the incompleteness of Einstein...This paper provides a perception that all things are connected. Starting with the perception of Metaphysics and how matter exists out of total void, complementary matter, or dark matter, the incompleteness of Einstein relativistic theory. Multi spacetime universes and the jump drive for jumping within and between spacetime universes. Warp drive for space travel. DNA as sequence of momentary frequencies and how it related to Ezekiel’s dry bones prophecy. The outcome of neural patterns as cords of consciousness, consciousness as the collection of all cords of consciousness and the lack of uniqueness of individual consciousness. Finally, all things are cords of consciousness.展开更多
Changes of neural firing patterns and transitions between firing patterns induced by the introduction of external stimulation or adjustment of biological parameter have been demonstrated to play key roles in informati...Changes of neural firing patterns and transitions between firing patterns induced by the introduction of external stimulation or adjustment of biological parameter have been demonstrated to play key roles in information coding.In this paper,bifurcation processes of bursting patterns were observed from an experimental neural pacemaker,through the adjustment of potassium parameter including ion concentration and calcium-dependent channel conductance.The adjustment of calcium-dependent potassium channel conductance was achieved by changing the extracellular tetraethylammonium concentration.The deterministic dynamics of chaotic bursting patterns induced by period-doubling bifurcation and intermittency,and lying between two periodic bursting patterns in a period-adding bifurcation process was investigated with a nonlinear prediction method.The bifurcations included period-doubling and period-adding bifurcations of bursting patterns.The experimental bifurcations and chaos closely matched those previously simulated in the theoretical neuronal model by adjusting potassium parameter,which demonstrated the simulation results of the theoretical model.The experimental results indicate that the potassium concentration and conductance of calcium-dependent potassium channel can induce bifurcations of the neural firing patterns.The potential role of these bifurcation structures in neural information coding mechanism is discussed.展开更多
The sensing of hot and cold stimuli by dental neurons differs in several fundamental ways. These sensations have been characterized quantitatively through the measured time course of neural discharge signals that resu...The sensing of hot and cold stimuli by dental neurons differs in several fundamental ways. These sensations have been characterized quantitatively through the measured time course of neural discharge signals that result from hot or cold stimuli applied to the teeth of animal models. Although various hypotheses have been proposed to explain the underlying mechanism, the ability to test competing hypotheses against experimental recorded data using biophysical models has been hindered by limitations in our understanding of the specific ion channels involved in nociception of dental neurons. Here we apply recent advances in established biophysical models to test the competing hypotheses. We show that a sharp shooting pain sensation experienced shortly following cold stimulation cannot be attributed to the activation of thermosensitive ion channels, thereby falsifying the so-called neuronal hypothesis, which states that rapidly transduced sensations of coldness are related to thermosensitive ion channels. Our results support a central role of mechanosensitive ion channels and the associated hydrodynamic hypothesis. In addition to the hydrodynamic hypothesis, we also demonstrate that the long time delay of dental neuron responses after hot stimulation could be attributed to the neuronal hypothesis-that a relatively long time is required for the temperature around nociceptors to reach some threshold. The results are useful as a model of how multiphysical phenomena can be combined to provide mechanistic insight into different mechanisms underlying pain sensations.展开更多
Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized...Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.10774088,10772101,30770701 and 10875076)the Fundamental Research Funds for the Central Universities(Grant No.GK200902025)
文摘To further identify the dynamics of the period-adding bifurcation scenarios observed in both biological experiment and simulations with differential Chay model, this paper fits a discontinuous map of a slow control variable of Chay model based on simulation results. The procedure of period adding bifurcation scenario from period k to period k ＋ 1 bursting （k = 1, 2, 3, 4） involved in the period-adding cascades and the stochastic effect of noise near each bifurcation point is also reproduced in the discontinuous map. Moreover, dynamics of the border-collision bifurcation is identified in the discontinuous map, which is employed to understand the experimentally observed period increment sequence. The simple discontinuous map is of practical importance in modeling of collective behaviours of neural populations like synchronization in large neural circuits.
文摘Neural firing patterns are investigated by using symbolic dynamics. Bifurcation behaviour of the Hindmarsh-Rose （HR） neuronal model is simulated with the external stimuli gradually decreasing, and various firing activities with different topological structures are orderly numbered. Through constructing first-return maps of interspike intervals, all firing patterns are described and identified by symbolic expressions. On the basis of ordering rules of symbolic sequences, the corresponding relation between parameters and firing patterns is established, which will be helpful for encoding neural information. Moreover, using the operation rule of ＊ product, generation mechanisms and intrinsic configurations of periodic patterns can be distinguished in detail. Results show that the symbolic approach is a powerful tool to study neural firing activities. In particular, such a coarse-grained way can be generalized in neural electropt/ysiological experiments to extract much valuable information from complicated experimental data.
基金supported by the Japan Society for the Promotion of Science(JSPS)KAKENHI under Grant JP22H03643Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)under Grant JPMJSP2145JST through the Establishment of University Fellowships towards the Creation of Science Technology Innovation under Grant JPMJFS2115.
文摘Residual neural network (ResNet) is a powerful neural network architecture that has proven to be excellent in extracting spatial and channel-wise information of images. ResNet employs a residual learning strategy that maps inputs directly to outputs, making it less difficult to optimize. In this paper, we incorporate differential information into the original residual block to improve the representative ability of the ResNet, allowing the modified network to capture more complex and metaphysical features. The proposed DFNet preserves the features after each convolutional operation in the residual block, and combines the feature maps of different levels of abstraction through the differential information. To verify the effectiveness of DFNet on image recognition, we select six distinct classification datasets. The experimental results show that our proposed DFNet has better performance and generalization ability than other state-of-the-art variants of ResNet in terms of classification accuracy and other statistical analysis.
基金Supported by National Natural Science Foundation of China (Grant No.11972129)National Science and Technology Major Project of China (Grant No.2017-IV-0008-0045)+1 种基金Heilongjiang Provincial Natural Science Foundation (Grant No.YQ2022A008)the Fundamental Research Funds for the Central Universities。
文摘The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.
基金Supported by the Ministerial Level Research Foundation(404040401)
文摘Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.
文摘This paper provides a perception that all things are connected. Starting with the perception of Metaphysics and how matter exists out of total void, complementary matter, or dark matter, the incompleteness of Einstein relativistic theory. Multi spacetime universes and the jump drive for jumping within and between spacetime universes. Warp drive for space travel. DNA as sequence of momentary frequencies and how it related to Ezekiel’s dry bones prophecy. The outcome of neural patterns as cords of consciousness, consciousness as the collection of all cords of consciousness and the lack of uniqueness of individual consciousness. Finally, all things are cords of consciousness.
基金supported by the National Natural Science Foundation of China(Grant Nos.11372224 and 11072135)the Fundamental Research Funds for Central Universities designated to Tongji University(Grant No.1330219127)
文摘Changes of neural firing patterns and transitions between firing patterns induced by the introduction of external stimulation or adjustment of biological parameter have been demonstrated to play key roles in information coding.In this paper,bifurcation processes of bursting patterns were observed from an experimental neural pacemaker,through the adjustment of potassium parameter including ion concentration and calcium-dependent channel conductance.The adjustment of calcium-dependent potassium channel conductance was achieved by changing the extracellular tetraethylammonium concentration.The deterministic dynamics of chaotic bursting patterns induced by period-doubling bifurcation and intermittency,and lying between two periodic bursting patterns in a period-adding bifurcation process was investigated with a nonlinear prediction method.The bifurcations included period-doubling and period-adding bifurcations of bursting patterns.The experimental bifurcations and chaos closely matched those previously simulated in the theoretical neuronal model by adjusting potassium parameter,which demonstrated the simulation results of the theoretical model.The experimental results indicate that the potassium concentration and conductance of calcium-dependent potassium channel can induce bifurcations of the neural firing patterns.The potential role of these bifurcation structures in neural information coding mechanism is discussed.
基金supported by the National Natural Science Foundation of China (Grants 11372243, 11522219, 11532009, and 11402192)the Fundamental Research Funds for the Central Universities (Grants 2016qngz03, 2015qngz09)the Openning Project of Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research,College of Stomatology, Xi’an Jiaotong University (Grant 2016LHM-KFKT007)
文摘The sensing of hot and cold stimuli by dental neurons differs in several fundamental ways. These sensations have been characterized quantitatively through the measured time course of neural discharge signals that result from hot or cold stimuli applied to the teeth of animal models. Although various hypotheses have been proposed to explain the underlying mechanism, the ability to test competing hypotheses against experimental recorded data using biophysical models has been hindered by limitations in our understanding of the specific ion channels involved in nociception of dental neurons. Here we apply recent advances in established biophysical models to test the competing hypotheses. We show that a sharp shooting pain sensation experienced shortly following cold stimulation cannot be attributed to the activation of thermosensitive ion channels, thereby falsifying the so-called neuronal hypothesis, which states that rapidly transduced sensations of coldness are related to thermosensitive ion channels. Our results support a central role of mechanosensitive ion channels and the associated hydrodynamic hypothesis. In addition to the hydrodynamic hypothesis, we also demonstrate that the long time delay of dental neuron responses after hot stimulation could be attributed to the neuronal hypothesis-that a relatively long time is required for the temperature around nociceptors to reach some threshold. The results are useful as a model of how multiphysical phenomena can be combined to provide mechanistic insight into different mechanisms underlying pain sensations.
基金supported by the National Natural Science Foundation of China,No.31070758,31271060the Natural Science Foundation of Chongqing in China,No.cstc2013jcyj A10085
文摘Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.
