The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.
Sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection, and is the leading cause of death in intensive care unit patients due to limited therapies currently. As is known to all, endotoxin is important in the pathogenesis of sepsis, whereby infection triggers a systemic inflammatory response, resulting in release of pro- and anti-inflammatory cytokines, which are thought to be responsible for the tissue damage that occurs in sepsis patients. Therefore, removing endotoxins is considered as an effective way to improve the conditions of sepsis patients. Both Toraymaxin (PMX) and adsorptive membrane such as oXiris can remove endotoxins by adsorption, and in this review we will summarize current studies, included in vitro study, animal study, and clinical research to show the benefit from endotoxin removal by oXiris, and also give some suggestions about oXiris clinical practice from experienced experts.
Both spike and local field potential (LFP) signals are two of the most important candidate signals for neural decoding. At present there are numerous studies on their decoding performance in mammals, but the decoding performance in birds is still not clear. We analyzed the decoding performance of both signals recorded from nidopallium caudolaterale area in six pigeons during the goal-directed decision-making task using the decoding algorithm combining leave-one-out and k-nearest neighbor (LOO-kNN). And the influence of the parameters, include the number of channels, the position and size of decoding window, and the nearest neighbor k value, on the decoding performance was also studied. The results in this study have shown that the two signals can effectively decode the movement intention of pigeons during the this task, but in contrast, the decoding performance of LFP signal is higher than that of spike signal and it is less affected by the number of channels. The best decoding window is in the second half of the goal-directed decision-making process, and the optimal decoding window size of LFP signal (0.3 s) is shorter than that of spike signal (1 s). For the LOO-kNN algorithm, the accuracy is inversely proportional to the k value. The smaller the k value is, the larger the accuracy of decoding is. The results in this study will help to parse the neural information processing mechanism of brain and also have reference value for brain-computer interface.
As an interface between external electronic devices and internal neural nuclei, microelectrodes play an important role in many fields, such as animal robots, deep brain stimulation and neural prostheses. Aiming at the problem of high price and complicated fabrication process of microelectrode, a microelectrode twisting machine based on open source electronic prototyping platform (Arduino) and three-dimensional printing technology was proposed, and its microelectrode fabrication performance and neural stimulation performance were verified. The results show that during the fabrication of microelectrodes, the number of positive twisting turns of the electrode wire should generally be set to about 1.8 times of its length, and the number of reverse twisting rings is independent of the length, generally about 5. Moreover, compared with the traditional instrument, the device is not only inexpensive and simple to manufacture, but also has good expandability. It has a positive significance for both the personalization and popularization of microelectrode fabrication and the reduction of experimental cost.
With the rapid improvement of the perception and computing capacity of mobile devices such as smart phones, human activity recognition using mobile devices as the carrier has been a new research hot-spot. The inertial information collected by the acceleration sensor in the smart mobile device is used for human activity recognition. Compared with the common computer vision recognition, it has the following advantages: convenience, low cost, and better reflection of the essence of human motion. Based on the WISDM data set collected by smart phones, the inertial navigation information and the deep learning algorithm-convolutional neural network (CNN) were adopted to build a human activity recognition model in this paper. The K nearest neighbor algorithm (KNN) and the random forest algorithm were compared with the CNN network in the recognition accuracy to evaluate the performance of the CNN network. The classification accuracy of CNN model reached 92.73%, which was much higher than KNN and random forest. Experimental results show that the CNN algorithm model can achieve more accurate human activity recognition and has broad application prospects in predicting and promoting human health.
Place cell with location tuning characteristics play an important role in brain spatial cognition and navigation, but there is relatively little research on place cell screening and its influencing factors. Taking pigeons as model animals, the screening process of pigeon place cell was given by using the spike signal in pigeon hippocampus under free activity. The effects of grid number and filter kernel size on the place field of place cells during the screening process were analyzed. The results from the real and simulation data showed that the proposed place cell screening method presented in this study could effectively screen out place cell, and the research found that the size of place field was basically inversely proportional to the number of grids divided, and was basically proportional to the size of Gaussian filter kernel in the overall trend. This result will not only help to determine the appropriate parameters in the place cell screening process, but also promote the research on the neural mechanism of spatial cognition and navigation of birds such as pigeons.
Animal localization and trajectory tracking are of great value for the study of brain spatial cognition and navigation neural mechanisms. However, traditional optical lens video positioning techniques are limited in their scope due to factors such as camera perspective. For pigeons with excellent spatial cognition and navigation abilities, based on the beacon positioning technology, a three-dimensional (3D) trajectory positioning and tracking method suitable for large indoor spaces was proposed, and the corresponding positioning principle and hardware structure were provided. The results of in vitro and in vivo experiments showed that the system could achieve centimeter-level positioning and trajectory tracking of pigeons in a space of 360 cm × 200 cm × 245 cm. Compared with traditional optical lens video positioning techniques, this system has the advantages of large space, high precision, and high response speed. It not only helps to study the neural mechanisms of pigeon 3D spatial cognition and navigation, but also has high reference value for trajectory tracking of other animals.
The neural stimulator is a core component of animal robots. While the control effect of animal robots is influenced by various factors, the performance of the neural stimulator plays a decisive role in regulating animal robots. In order to optimize animal robots, embedded neural stimulators had been developed using flexible printed circuit board technology. This innovation not only enabled the stimulator to generate parameter-adjustable biphasic current pulses through control signals, but also optimized its carrying mode, material, and size, overcoming the disadvantages of traditional backpack or head-inserted stimulators, which have poor concealment and are prone to infection. Static, in vitro, and in vivo performance tests of the stimulator demonstrated that it not only had precise pulse waveform output capability, but also was lightweight and small in size. It had excellent in vivo performance in both laboratory and outdoor environments. Our study has high practical significance for the application of animal robots.
Systematic review (SR) and meta-analysis, as the highest level of evidence-based medicine, are an indispensable part of guiding medical staff to make medical decisions. At the same time, the status of patients as shared decision-making is rising. At present, the results of SR and meta-analysis are mainly presented in the form of effect (relative risk or mean difference) and forest plot. The expression is not intuitive or professional. The process of evidence-based evidence guiding clinical decision-making lags behind, which cannot meet the needs of rapid decision-making. With the continuous progress in artificial intelligence and big data analysis tools, researchers have attempted to introduce visual presentations to improve the timeliness of clinical decision-making. Through the interpretation of the outcomes of SR and meta-analysis, this paper presents different visualization results from the perspective of patients and clinical decision-makers, which not only helps the majority of people without medical background understand clinical evidence more intuitively and participate in the process of clinical decision-making, but also helps improve residents' health literacy, promotes the dissemination and sharing of knowledge, and provides references for further promoting the technology of automatic decision-making system.