Progress of Micro-Stimulation Techniques to Alter Pigeons' Motor Behavior: A Review from the Perspectives of the Neural Basis and Neuro-Devices.

Pigeons have natural advantages in robotics research, including a wide range of activities, low energy consumption, good concealment performance, strong long-distance weight bearing and continuous flight ability, excellent navigation, and spatial cognitive ability, etc. They are typical model animals in the field of animal robot research and have important application value. A hot interdisciplinary research topic and the core content of pigeon robot research, altering pigeon motor behavior using brain stimulation involves multiple disciplines including animal ethology, neuroscience, electronic information technology and artificial intelligence technology, etc. In this paper, we review the progress of altering pigeon motor behavior using brain stimulation from the perspectives of the neural basis and neuro-devices. The recent literature on altering pigeon motor behavior using brain stimulation was investigated first. The neural basis, structure and function of a system to alter pigeon motor behavior using brain stimulation are briefly introduced below. Furthermore, a classified review was carried out based on the representative research achievements in this field in recent years. Our summary and discussion of the related research progress cover five aspects including the control targets, control parameters, control environment, control objectives, and control system. Future directions that need to be further studied are discussed, and the development trend in altering pigeon motor behavior using brain stimulation is projected.

Decoding Typical Flight States Based on Neural Signals from the Midbrain Motor Nuclei of Pigeons.

BACKGROUND: Exploring the neural encoding mechanism and decoding of motion state switching during flight can advance our knowledge of avian behavior control and contribute to the development of avian robots. However, limited acquisition equipment and neural signal quality have posed challenges, thus we understand little about the neural mechanisms of avian flight. METHODS: We used chronically implanted micro-electrode arrays to record the local field potentials (LFPs) in the formation reticularis medialis mesencephali (FRM) of pigeons during various motion states in their natural outdoor flight. Subsequently, coherence-based functional connectivity networks under different bands were constructed and the topological features were extracted. Finally, we used a support vector machine model to decode different flight states. RESULTS: Our findings indicate that the gamma band (80-150 Hz) in the FRM exhibits significant power for identifying different states in pigeons. Specifically, the avian brain transmitted flight related information more efficiently during the accelerated take-off or decelerated landing states, compared with the uniform flight and baseline states. Finally, we achieved a best average accuracy of 0.86 using the connectivity features in the 80-150 Hz band and 0.89 using the fused features for state decoding. CONCLUSIONS: Our results open up possibilities for further research into the neural mechanism of avian flight and contribute to the understanding of flight behavior control in birds.

The Hippocampus in Pigeons Contributes to the Model-Based Valuation and the Relationship between Temporal Context States.

Model-based decision-making guides organism behavior by the representation of the relationships between different states. Previous studies have shown that the mammalian hippocampus (Hp) plays a key role in learning the structure of relationships among experiences. However, the hippocampal neural mechanisms of birds for model-based learning have rarely been reported. Here, we trained six pigeons to perform a two-step task and explore whether their Hp contributes to model-based learning. Behavioral performance and hippocampal multi-channel local field potentials (LFPs) were recorded during the task. We estimated the subjective values using a reinforcement learning model dynamically fitted to the pigeon's choice of behavior. The results show that the model-based learner can capture the behavioral choices of pigeons well throughout the learning process. Neural analysis indicated that high-frequency (12-100 Hz) power in Hp represented the temporal context states. Moreover, dynamic correlation and decoding results provided further support for the high-frequency dependence of model-based valuations. In addition, we observed a significant increase in hippocampal neural similarity at the low-frequency band (1-12 Hz) for common temporal context states after learning. Overall, our findings suggest that pigeons use model-based inferences to learn multi-step tasks, and multiple LFP frequency bands collaboratively contribute to model-based learning. Specifically, the high-frequency (12-100 Hz) oscillations represent model-based valuations, while the low-frequency (1-12 Hz) neural similarity is influenced by the relationship between temporal context states. These results contribute to our understanding of the neural mechanisms underlying model-based learning and broaden the scope of hippocampal contributions to avian behavior.

Phase-Amplitude Coupling between Theta Rhythm and High-Frequency Oscillations in the Hippocampus of Pigeons during Navigation.

Navigation is a complex task in which the hippocampus (Hp), which plays an important role, may be involved in interactions between different frequency bands. However, little is known whether this cross-frequency interaction exists in the Hp of birds during navigation. Therefore, we examined the electrophysiological characteristics of hippocampal cross-frequency interactions of domestic pigeons (Columba livia domestica) during navigation. Two goal-directed navigation tasks with different locomotor modes were designed, and the local field potentials (LFPs) were recorded for analysis. We found that the amplitudes of high-frequency oscillations in Hp were dynamically modulated by the phase of co-occurring theta-band oscillations both during ground-based maze and outdoor flight navigation. The high-frequency amplitude sub-frequency bands modulated by the hippocampal theta phase were different at different tasks, and this process was independent of the navigation path and goal. These results suggest that phase-amplitude coupling (PAC) in the avian Hp may be more associated with the ongoing cognitive demands of navigational processes. Our findings contribute to the understanding of potential mechanisms of hippocampal PAC on multi-frequency informational interactions in avian navigation and provide valuable insights into cross-species evolution.

Dynamics Learning Rate Bias in Pigeons: Insights from Reinforcement Learning and Neural Correlates.

Research in reinforcement learning indicates that animals respond differently to positive and negative reward prediction errors, which can be calculated by assuming learning rate bias. Many studies have shown that humans and other animals have learning rate bias during learning, but it is unclear whether and how the bias changes throughout the entire learning process. Here, we recorded the behavior data and the local field potentials (LFPs) in the striatum of five pigeons performing a probabilistic learning task. Reinforcement learning models with and without learning rate biases were used to dynamically fit the pigeons' choice behavior and estimate the option values. Furthemore, the correlation between the striatal LFPs power and the model-estimated option values was explored. We found that the pigeons' learning rate bias shifted from negative to positive during the learning process, and the striatal Gamma (31 to 80 Hz) power correlated with the option values modulated by dynamic learning rate bias. In conclusion, our results support the hypothesis that pigeons employ a dynamic learning strategy in the learning process from both behavioral and neural aspects, providing valuable insights into reinforcement learning mechanisms of non-human animals.

Application of deep reinforcement learning for spike sorting under multi-class imbalance.

Spike sorting is the basis for analyzing spike firing patterns encoded in high-dimensional information spaces. With the fact that high-density microelectrode arrays record multiple neurons simultaneously, the data collected often suffers from two problems: a few overlapping spikes and different neuronal firing rates, which both belong to the multi-class imbalance problem. Since deep reinforcement learning (DRL) assign targeted attention to categories through reward functions, we propose ImbSorter to implement spike sorting under multi-class imbalance. We describe spike sorting as a Markov sequence decision and construct a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. The agent is eventually guided by the optimal strategy to classify spikes. We consider the Wave_Clus dataset, which contains overlapping spikes and diverse noise levels, and the macaque dataset, which has a multi-scale imbalance. ImbSorter is compared with classical DRL architectures, traditional machine learning algorithms, and advanced overlapping spike sorting techniques on these two above datasets. ImbSorter obtained improved results on the Macro_F1. The results show ImbSorter has a promising ability to resist overlapping and noise interference. It has high stability and promising performance in processing spikes with different degrees of skewed distribution.

Goal given moment modulates the time period of gamma oscillations in nidopallium caudolaterale during the goal-directed behavior of pigeon.

The cognitive processes of goal-directed navigation are believed to be organized around and serve the identification and selection of goals. Differences in LFP signals in avian nidopallium caudolaterale (NCL) under different goal location/distance information in the goal-directed behavior have been studied. However, for goals that are multifarious constructs that include various information, the modulation of goal time information on the LFP of NCL during goal-directed behavior remains unclear. In this study, we recorded the LFP activity from the NCL of eight pigeons as they performed two goal-directed decision-making tasks in a plus-maze. During the two tasks with different goal time information, spectral analysis revealed significant LFP power selectively enhanced in the slow gamma band (40-60 Hz), while the slow gamma band of LFP which could effectively decode the behavioral goal of the pigeons existed in different time periods. These findings suggest that the LFP activity in the gamma band correlates with the goal-time information, and help to shed light on the contribution of the gamma rhythm recorded from the NCL in goal-directed behavior.

Research of control method for pneumatic control of pneumatic microchips.

A novel composite control method for actuated chamber air pressure of pneumatic microfluidic chip via a three-way electromagnetic microvalve is presented in this paper. The purpose of the control methods is to improve air pressure controlling precision for pneumatic control. By using the Bang-Bang (on-off) controller for pneumatic control, the step-response time, air pressure steady-state accuracy, and air pressure fluctuations are performed with different maximum thresholds and minimum thresholds. Moreover, by using the k (proportional ) plus PWM (Pulse-Width Modulation) control method for pneumatic control, the step-response time, air pressure steady-state accuracy, and air pressure fluctuations are performed with different carrier frequencies and carrier amplitudes. Both advantages and disadvantages of the two control methods are compared and analyzed based on the experimental data. According to the variable volume of the actuated chamber and the response characteristics of the three-way electromagnetic microvalve, the composite control method of the Bang-Bang plus k plus PWM is developed to control the actuated chamber air pressure. The experimental results show that when the absolute air pressure of the actuated chamber is set to 150kPa, the rising time is 69.3ms, which is about 8.0ms shorter than that of the k+PWM control method alone. The steady-state error is reduced from 0.90kPa to 0.65kPa, and the air pressure steady-state fluctuation is reduced from 1.60kPa to 0.90kPa, compared with the Bang-Bang control method alone.

Elevated Gamma Connectivity in Nidopallium Caudolaterale of Pigeons during Spatial Path Adjustment.

Previous studies showed that spatial navigation depends on a local network including multiple brain regions with strong interactions. However, it is still not fully understood whether and how the neural patterns in avian nidopallium caudolaterale (NCL), which is suggested to play a key role in navigation as a higher cognitive structure, are modulated by the behaviors during spatial navigation, especially involved path adjustment needs. Hence, we examined neural activity in the NCL of pigeons and explored the local field potentials' (LFPs) spectral and functional connectivity patterns in a goal-directed spatial cognitive task with the detour paradigm. We found the pigeons progressively learned to solve the path adjustment task when the learned path was blocked suddenly. Importantly, the behavioral changes during the adjustment were accompanied by the modifications in neural patterns in the NCL. Specifically, the spectral power in lower bands (1-4 Hz and 5-12 Hz) decreased as the pigeons were tested during the adjustment. Meanwhile, an elevated gamma (31-45 Hz and 55-80 Hz) connectivity in the NCL was also detected. These results and the partial least square discriminant analysis (PLS-DA) modeling analysis provide insights into the neural activities in the avian NCL during the spatial path adjustment, contributing to understanding the potential mechanism of avian spatial encoding. This study suggests the important role of the NCL in spatial learning, especially path adjustment in avian navigation.

Many heads are better than one: A multiscale neural information feature fusion framework for spatial route selections decoding from multichannel neural recordings of pigeons.

The neural information at different scales exhibits spatial representations and the corresponding features are believed to be conducive for neural encoding. However, existing neural decoding studies on multiscale feature fusion have rarely been investigated. In this study, a multiscale neural information feature fusion framework is presented and we integrate these features to decode spatial routes from multichannel recordings. We design a goal-directed spatial cognitive experiment in which the pigeons need to perform a route selection task. Multichannel neural activities including spike and local field potential (LFP) recordings in the hippocampus are recorded and analyzed. The multiscale neural information features including spike firing rate features, LFP time-frequency energy features, and functional network connectivity features are extracted for spatial route decoding. Finally, we fuse the multiscale feature to solve the neural decoding problem and the results indicate that feature fusion operation improves the decoding performance significantly. Ten-fold cross-validation result analysis shows a promising improvement in the decoding performance using fusing multiscale features by an average of 0.04-0.11 at least than using any individual feature set alone. The proposed framework investigates the possibility of route decoding based on multiscale features, providing an effective way to solve the neural information decoding problems.

Refined Multiscale Entropy Analysis of Wrist Pulse for Gender Difference in Traditional Chinese Medicine among Young Individuals.

Pulse signal analysis plays an important role in promoting the objectification of traditional Chinese medicine (TCM). Like electrocardiogram (ECG) signals, wrist pulse signals are mainly caused by cardiac activities and are valuable in analyzing cardiac diseases. A large number of studies have reported ECG signals can distinguish gender characteristics of normal healthy subjects using entropy complexity measures, consistently showing more complexity in females than males. No research up to date, however, has been found on examining gender differences with wrist pulse signals of healthy subjects on entropy complexity measures. This paper is aimed to fill in the research gap, which could, in turn, provide a deeper insight into the pulse signal and might identify potential differences between ECG signals and pulse signals. In particular, several complementary entropy measures with corresponding refined composite multiscale versions are established to perform the analysis for the filtered TCM pulse signals. Experimental results reveal that regardless of entropy measures used, there is no statistically significant gender difference in terms of entropy complexity, indicating that the pulse signal holds less gender characteristics than the ECG signal. In view of these findings, wrist pulse signals could be likely to provide different pieces of information to ECG signals. The present study is the first to quantitatively evaluate gender differences in healthy pulse signals with measures of entropy complexity and more importantly; we expect this study could make contribution to the ongoing pulse intelligent diagnosis and objective analysis, further facilitating the modernization of TCM pulse diagnosis.

Disarrangement and reorganization of the hippocampal functional connectivity during the spatial path adjustment of pigeons.

BACKGROUND: The hippocampus plays an important role to support path planning and adjustment in goal-directed spatial navigation. While we still only have limited knowledge about how do the hippocampal neural activities, especially the functional connectivity patterns, change during the spatial path adjustment. In this study, we measured the behavioural indicators and local field potentials of the pigeon (Columba livia, male and female) during a goal-directed navigational task with the detour paradigm, exploring the changing patterns of the hippocampal functional network connectivity of the bird during the spatial path learning and adjustment. RESULTS: Our study demonstrates that the pigeons progressively learned to solve the path adjustment task after the preferred path is blocked suddenly. Behavioural results show that both the total duration and the path lengths pigeons completed the task during the phase of adjustment are significantly longer than those during the acquisition and recovery phases. Furthermore, neural results show that hippocampal functional connectivity selectively changed during path adjustment. Specifically, we identified depressed connectivity in lower bands (delta and theta) and elevated connectivity in higher bands (slow-gamma and fast-gamma). CONCLUSIONS: These results feature both the behavioural response and neural representation of the avian spatial cognitive learning process, suggesting that the functional disarrangement and reorganization of the connectivity in the avian hippocampus during different phases may contribute to our further understanding of the potential mechanism of path learning and adjustment.

Spatial learning correlates with decreased hippocampal activity in the goal-directed behavior of pigeons.

Studies have suggested that the hippocampus (Hp) plays an important role in spatial learning and avian Hp is thought to have similar functions with mammals. However, the dynamic neural pattern of hippocampal activity is still unclear in the continuous spatial learning processes of birds. In this study, we recorded the behavioral data and local field potential (LFP) activity from Hp of pigeons performing goal-directed behavior. Then the spectral properties and time-frequency properties of the LFPs are analyzed, comparing with the behavioral changes during spatial learning. The results indicated that the power of the LFP signal in the gamma band shown decreasing trend during spatial learning. Time-frequency analysis results shown that the hippocampal gamma activity was weakened along with the learning process. The results indicate that spatial learning correlated with the decreased gamma activity in Hp and hippocampal neural patterns of pigeons were modulated by goal-directed behavior.

Enhanced Hippocampus-Nidopallium Caudolaterale Connectivity during Route Formation in Goal-Directed Spatial Learning of Pigeons.

Goal-directed spatial learning is crucial for the survival of animals, in which the formation of the route from the current location to the goal is one of the central problems. A distributed brain network comprising the hippocampus and prefrontal cortex has been shown to support such capacity, yet it is not fully understood how the most similar brain regions in birds, the hippocampus (Hp) and nidopallium caudolaterale (NCL), cooperate during route formation in goal-directed spatial learning. Hence, we examined neural activity in the Hp-NCL network of pigeons and explored the connectivity dynamics during route formation in a goal-directed spatial task. We found that behavioral changes in spatial learning during route formation are accompanied by modifications in neural patterns in the Hp-NCL network. Specifically, as pigeons learned to solve the task, the spectral power in both regions gradually decreased. Meanwhile, elevated hippocampal theta (5 to 12 Hz) connectivity and depressed connectivity in NCL were also observed. Lastly, the interregional functional connectivity was found to increase with learning, specifically in the theta frequency band during route formation. These results provide insight into the dynamics of the Hp-NCL network during spatial learning, serving to reveal the potential mechanism of avian spatial navigation.

Network Pharmacology for Analyzing the Key Targets and Potential Mechanism of Wogonin in Gliomas.

Objective: To analyze the key targets and potential mechanisms underlying the volatile components of Scutellaria baicalensis Georgi acting on gliomas through network pharmacology combined with biological experiments. Methods: We have extracted the volatile components of Scutellaria baicalensis by gas chromatography-mass spectrometry (GC-MS) and determined the active components related to the onset and development of gliomas by combining the results with the data from the Traditional Chinese Medicine Systems Pharmacology database. We screened the same targets for the extracted active components and gliomas through network pharmacology and then constructed a protein-protein interaction network. Using a Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we analyzed the protein effects and regulatory pathways of the common targets. Lastly, we employed ELISA and Western blot in verifying the key targets in the regulatory pathway. Results: We ultimately determined that the active component in S. baicalensis Georgi related to the onset and development of gliomas was Wogonin. The results of the network pharmacology revealed 85 targets for glioma and Wogonin. We used gene ontology to analyze these target genes and found that they involved 30 functions, such as phosphatidylinositol phosphokinase activation, while the KEGG analysis showed that there were 10 regulatory pathways involved. Through the following analysis, we found that most of the key target genes are distributed in the PI3K-Akt and interleukin 17 signaling pathways. We then cultured U251 glioma cells for the experiments. Compared with the control group, no significant change was noted in the caspase-3 expression; however, cleaved caspase-3 expression increased significantly and was dose-dependent on Wogonin. The expression of Bad and Bcl-2 with 25 µM of Wogonin has remained unchanged, but when the Wogonin dose was increased to 100 µM, the expression of Bad and Bcl-2 was noted to change significantly (Bad was significantly upregulated, while Bcl-2 was significantly downregulated) and was dose-dependent on Wogonin. The ELISA results showed that, compared with the control group, the secretion of tumor necrosis factor alpha, IL-1ß, and IL-6 decreased as the Wogonin concentration increased. Tumor necrosis factor alpha downregulation had no significant dose-dependent effect on Wogonin, the inhibitory effect of 25 µM of Wogonin on IL-6 was not significant, and IL-1ß downregulation had a significant dose-dependent effect on Wogonin. Conclusion: Wogonin might promote the apoptosis of glioma cells by upregulating proapoptotic factors, downregulating antiapoptotic factors, and inhibiting the inflammatory response, thereby inhibiting glioma progression.

Decoding route selection of pigeon during goal-directed behavior: A joint spike-LFP study.

How to reach the goal is one of the core problems that animals must solve to complete goal-directed behavior. Studies have proved the important role of hippocampus (Hp) in spatial navigation and shown that hippocampal neural activities can represent the current location and goal location. However, for the different routes linking these two locations, the neural representation mechanism of the route selection in Hp is not clear. Here, we addressed this question using neural recordings of Hp ensembles and decoding analyses in pigeons performing a goal-directed route selection task known to require Hp participation. The hippocampal spike trains and local field potentials (LFPs) of five pigeons performing the task were acquired and analyzed. We found that the neuron firing rates and power spectrum characteristics in Hp could encode the animal's route selection during goal-directed behavior, suggesting that the representation of route selection was coherent for hippocampal spike and LFP signals. Decoding results further indicated that joint spike-LFP features resulted in a significant improvement in the representation accuracy of the route selection. These findings of this study will help to understand the encoding mechanism of route selection in goal-directed behavior.

The Role of Hp-NCL Network in Goal-Directed Routing Information Encoding of Bird: A Review.

Goal-directed navigation is a crucial behavior for the survival of animals, especially for the birds having extraordinary spatial navigation ability. In the studies of the neural mechanism of the goal-directed behavior, especially involving the information encoding mechanism of the route, the hippocampus (Hp) and nidopallium caudalle (NCL) of the avian brain are the famous regions that play important roles. Therefore, they have been widely concerned and a series of studies surrounding them have increased our understandings of the navigation mechanism of birds in recent years. In this paper, we focus on the studies of the information encoding mechanism of the route in the avian goal-directed behavior. We first summarize and introduce the related studies on the role of the Hp and NCL for goal-directed behavior comprehensively. Furthermore, we review the related cooperative interaction studies about the Hp-NCL local network and other relevant brain regions supporting the goal-directed routing information encoding. Finally, we summarize the current situation and prospect the existing important questions in this field. We hope this paper can spark fresh thinking for the following research on routing information encoding mechanism of birds.

Functional connectivity changes in the intra- and inter-brain during the construction of the multi-brain network of pigeons.

Multi-brain network, also known as social cooperative network, is formed by multiple animal or human brains, whose changes of functional connectivity in the intra- and inter-brain during construction are unclear at present. To investigate the intra- and inter-brain functional connectivity of pigeons while performing a social cooperation task, we designed an inter-brain synchronization task to train three pigeons to synchronize their neural activities using cross-brain neurofeedback. Then the neural signals of three pigeons were simultaneously recorded by using a hyperscanning approach, and inter-brain synchronization was calculated using the phase-locked value (PLV) online. Finally, the intra- and inter-brain functional connectivity of three pigeons were analyzed. We found that during long-term neurofeedback training, with the increase of the inter-brain synchronization of three pigeons, the intra- and inter-brain functional connectivity also enhance significantly. Moreover, we also found that the above phenomenon relies on the external visual cue. These results suggest that the promotion of social cooperation is the result of the modulation between the intra- and inter-brain, which may be an underlying neural mechanism of the communication and cooperation among individuals in social networks.

Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning.

Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologies, radiomics provides increasing opportunities for clinical decision-making. Several studies have applied radiomics as a decision support tool in intracranial tumors differentiation. Here we seek to achieve preoperative differentiation between ependymoma (EP) and pilocytic astrocytoma (PA) using radiomics analysis method based on machine learning. A total of 135 Magnetic Resonance Imaging (MRI) slices are divided into training sets and validation sets. Three kinds of radiomics features, including Gabor transform, texture and wavelet transform based ones are used to obtain 300 multimodal features. Kruskal-Wallis test score (KWT) and support vector machines (SVM) are applied for feature selection and tumor differentiation. The performance is investigated via accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. Results show that the accuracy, sensitivity, specificity, and AUC of the selected feature set are 0.8775, 0.9292, 0.8000, and 0.8646 respectively, having no significantdifferencescomparedwiththe overall feature set. For different types of features, texture features yield the best differentiation performance and the significance analysis results are consistent with this. Our study demonstrates texture features perform better than the other features. The radiomics approach based on machine learning is efficient for pediatric posterior fossa tumors differentiation and could enhance the application of radiomics methods for assisted clinical diagnosis.

[A spike denoising method combined principal component analysis with wavelet and ensemble empirical mode decomposition].

Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 µV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 µV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.