Slow Gamma Activity of Local Field Potentials (LFP) in the Freely Moving Rat Relates to Movement

Quantitative assessment of local field potentials by means of Fast Fourier Transformation (FFT) results in the so-called power density spectrum. Within this spectrum particular frequency ranges are defined in order to relate these to behavior. Frequencies above 35 Hz are generally labeled as gamma oscillations, especially as low gamma (40 - 55 Hz) or high gamma (70 - 100 Hz). In order to learn more about this feature, we implanted a set of 4 bipolar concentric steel electrodes in frontal cortex, hippocampus, striatum and midbrain reticular formation of 10 rats. After recovery, field potentials were recorded and wirelessly transmitted to our computer for frequency analysis. At the same time, motion was registered during the whole experimental period of 5.75 hours. Results revealed that low gamma activity only emerged when the animal moved—at least his head. FFT of the data showed—besides other frequencies—a slow gamma activity peaking around 47 Hz pre-dominantly within the striatum, less in frontal cortex and reticular formation and nearly none in the hippocampus. Spectral analysis was performed for single epochs of 4 seconds and all 15 minutes intervals. Correlation analysis of these intervals was done to motion data. All rats showed a highly significant correlation between gamma activity and movement. We therefore conclude from these experiments that this slow gamma activity of the field potentials is not only related to movement, but possibly part of the general neuronal coding of movement.

Hippocampal ischemia causes deficits in local field potential and synaptic plasticity

The long-term enhancement in glutamate receptor mediated excitatory responses has been observed in stroke model. This pathological form of plasticity, termed post-ischemic long-term potentiation （i-LTP）, points to functional reorganization after stroke. Little is known, however, about whether and how this i-LTP would affect subsequent induction of synaptic plasticity. Here, we first directly confirmed that i-LTP was induced in the endothelin-l-induced ischemia model as in other in vitro models. We also demonstrated increased expression of NR2B, CaMKII and p-CaMKII, which are reminiscent of i-LTP. We further induced LTP of field excitatory post- synaptic potentials （fEPSPs） on CA1 hippocampal neurons in peri-infarct regions of the endothelin-l-induced mini-stroke model. We found that LTP of fEPSPs, induced by high-frequency stimulation, displayed a progressive impairment at 12 and 24 hours after ischemia. Moreover, using in vivo multi-channel recording, we found that the local field potential, which represents electrical property of cell ensembles in more restricted regions, was also dam- pened at these two time points. These results suggest that i-LTP elevates the induction threshold of subsequent synap- tic plasticity. Our data helps to deepen the knowledge of meta-synaptic regulation of plasticity after focal ischemia.

Virtual local target method for avoiding local minimum in potential field based robot navigation

A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.

The role of local field potential coupling in epileptic synchronization

This review hopes to clearly explain the following viewpoints： （1） Neuronal synchronization underlies brain functioning, and it seems possible that blocking excessive synchronization in an epileptic neural network could reduce or even control seizures. （2） Local field potential coupling is a very common phenomenon during synchronization in networks. Removal of neurons or neuronal networks that are coupled can significantly alter the extracellular field potential. Interventions of coupling mediated by local field potentials could result in desynchronization of epileptic seizures. （3） The synchronized electrical activity generated by neurons is sensitive to changes in the size of the extracellular space, which affects the efficiency of field potential transmission and the threshold of cell excitability. （4） Manipulations of the field potential fluctuations could help block synchronization at seizure onset.

Effect of propofol on local field potential in rat prefrontal cortex during working memory task

Propofol may produce memory impairment during anesthesia procedure. Local field potentials (LFPs) are used with increasing frequency in recent years to link neural activity to perception and cognition. In this study, effect of propofol on LFPs in rat’s prefrontal cortex during working memory task was evaluated. Young (approximately 3 month) male Sprague-Dawley rats were divided into two group: propofol rats and control rats. Propofol rats received propofol at 0.9 mg/Kg·min intravenously for 2 h. After 12 h, LFPs of all rats were measured simultaneously from multiple electrodes placed in prefrontal cortex while rats were performing a working memory task in Y-maze. LFPs instantaneous phase were obtained by applying Hilbert transform, and cross-correlation coherence of LFPs was calculated. The results indicate that propofol decreased the correct rate and crosscorrelation coherence of LFPs on the first two days (p 0.05). Our results suggest that propofol can impair cross-correlation coherence of LFPs in the first two days, but not long time.

Effect of focused ultrasound stimulation at different ultrasonic power levels on the local field potential power spectrum

Local field potential(LFP) signals of the rat hippocampus were recorded under noninvasive focused ultrasound stimulation(FUS) with different ultrasonic powers. The LFP mean absolute power was calculated with the Welch algorithm at the delta, theta, alpha, beta, and gamma frequency bands. The experimental results demonstrate that the LFP mean absolute power at different frequency bands increases as the ultrasound power increases.

Mechanistic roles of the subthalamic nucleus and internal globus pallidus:evidence from local field potentials and deep brain stimulation

Deep brain stimulation (DBS) has become an effective therapeutic option for neurological and psychiatric disorders such as Parkinson’s disease (PD), dystonia, and obsessive-compulsive disorder. The subthalamic nucleus (STN) and internal globus pallidus (GPi) are by far the most commonly used targets for DBS in the treatment of PD. However, STN/GPi stimulation sometimes causes side effects, including motor fluctuations, cognitive declines, and worse emotional experience, which affect patients’ postoperative quality of life. Recent invasive electrophysiological studies are driven by the desire to better understand the mechanisms of therapeutic actions and side effects of STN/GPi stimulation. These studies investigated the function of the STN and GPi in motor, cognitive and affective processes by recording single- neuron firing patterns during the surgery or local field potentials after the surgery. Here we review the relevant studies to provide an integrative picture of the functional roles of the STN and GPi within the basal ganglia loops for motor, cognition, and emotion. Previous studies suggested that STN and GPi gamma oscillations encode the strength and speed of voluntary movements (execution), whereas beta oscillations reflect the effort and demand of potential movements (preparation). In the cognitive domain, oscillatory beta activity in the STN is involved when people have to stop an inappropriate action or to suppress salient but task-irrelevant information, whereas theta/delta activity is associated with the adjustment of decision thresholds and cost-benefit trade-off. In the affective domain, STN activity in the alpha band may represent the valence and arousal of emotional information.

Multi-Behavior Fusion Based Potential Field Method for Path Planning of Unmanned Surface Vessel

The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.

Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.

Local minima-free design of artificial coordinating fields

In order to overcome the drawbacks of conventional artificial potential fields (APF) based methods for the motion planning problems of mobile robots in dynamic uncertain environments, an artificial coordinating fields (ACF) based method has been proposed recently. This paper deals with the reachability problem of the ACF, that is, how to design and choose the parameters of the ACF and how the environment should be such that the robot can reach its goal without being trapped in local minima. Some sufficient conditions for these purposes are developed theoretically. Theoretical analyses show that, the ACF can effectively remove local minima in dynamic uncertain environments with V-shape or U-shape obstacles, and guide the mobile robot to reach its goal with some necessary environment constraints and based on the methods provided in this paper to properly choose the parameters of the ACF. Comparisons between the ACF and APF, and simulations are provided to illustrate the advantages of the ACF.

Local field potentials,spiking activity,and receptive fields in human visual cortex

The concept of receptive field(RF) is central to sensory neuroscience. Neuronal RF properties have been substantially studied in animals,while those in humans remain nearly unexplored. Here, we measured neuronal RFs with intracranial local field potentials(LFPs) and spiking activity in human visual cortex(V1/V2/V3). We recorded LFPs via macro-contacts and discovered that RF sizes estimated from lowfrequency activity(LFA, 0.5–30 Hz) were larger than those estimated from low-gamma activity(LGA, 30–60 Hz) and high-gamma activity(HGA, 60–150 Hz). We then took a rare opportunity to record LFPs and spiking activity via microwires in V1 simultaneously. We found that RF sizes and temporal profiles measured from LGA and HGA closely matched those from spiking activity. In sum, this study reveals that spiking activity of neurons in human visual cortex could be well approximated by LGA and HGA in RF estimation and temporal profile measurement, implying the pivotal functions of LGA and HGA in early visual information processing.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Cortico-striatal gamma oscillations are modulated by dopamine D3 receptors in dyskinetic rats

Long-term levodopa administration can lead to the development of levodopa-induced dyskinesia.Gamma oscillations are a widely recognized hallmark of abnormal neural electrical activity in levodopa-induced dyskinesia.Currently,studies have reported increased oscillation power in cases of levodopa-induced dyskinesia.However,little is known about how the other electrophysiological parameters of gamma oscillations are altered in levodopa-induced dyskinesia.Furthermore,the role of the dopamine D3 receptor,which is implicated in levodopa-induced dyskinesia,in movement disorder-related changes in neural oscillations is unclear.We found that the cortico-striatal functional connectivity of beta oscillations was enhanced in a model of Parkinson’s disease.Furthermore,levodopa application enhanced cortical gamma oscillations in cortico-striatal projections and cortical gamma aperiodic components,as well as bidirectional primary motor cortex(M1)↔dorsolateral striatum gamma flow.Administration of PD128907(a selective dopamine D3 receptor agonist)induced dyskinesia and excessive gamma oscillations with a bidirectional M1↔dorsolateral striatum flow.However,administration of PG01037(a selective dopamine D3 receptor antagonist)attenuated dyskinesia,suppressed gamma oscillations and cortical gamma aperiodic components,and decreased gamma causality in the M1→dorsolateral striatum direction.These findings suggest that the dopamine D3 receptor plays a role in dyskinesia-related oscillatory activity,and that it has potential as a therapeutic target for levodopa-induced dyskinesia.

基于spike-LFP同步的神经元朝向选择性分析

局部场电位(local field potential,LFP)与神经元发放的锋电位(spike)之间的同步关系揭示了重要的神经编码信息。为了研究不同对比度的光栅刺激下猕猴视皮层V1区和V4区神经元的朝向选择性,本文将锋电位触发的相关矩阵同步方法应用于在猕猴视皮层V1区和V4区记录的微电极信号,发现spike与gamma频段LFP之间的同步与光栅朝向之间存在较强的相关性,反映了视觉刺激的基本特征信息。另外,神经元spike的发放速率虽然会受到光栅朝向的影响,但是在不同对比度下发放速率并无明显区别,而spike-LFP同步会随着对比度的增加而增加,能够反映出同一朝向下对比度的变化。本文还利用圆方差来量化spike-LFP同步的朝向调谐曲线,发现对比度越大,V1区和V4区神经元的朝向选择性越强,V1区神经元比V4区神经元具有更强的朝向选择性。总之,spike-LFP同步能够更深入地揭示视皮层神经元的朝向调谐特性,为研究朝向选择性的形成机制提供新的方向。

猕猴伸展抓握过程中的spike与LFP锁相相位分布特征分析

神经元动作电位(spike)的发放和局部场电位(local field potential,LFP)之间的锁相关系是重要的神经编码信息,但是目前的研究在连续运动过程中对锁相相位分布特征的分析仍比较缺乏.利用猕猴伸展抓握运动范式下的spike和LFP delta节律的锁相特性,分别在不同运动阶段,不同事件前后100 ms内,以及连续时间窗3种时间尺度下对M1、S1、PPC脑区的锁相相位分布变化进行了分析,实验发现在猕猴的连续运动中的不同阶段,spike和LFP的锁相相位分布具有不同的相位分布特点和相位分布峰值位置;对关键的事件TargetOn、CenterRelease、TargetHit发生前后各100 ms内的锁相相位分布进行分析,结果发现事件发生前后100 ms内的锁相相位分布具有显著差异性,锁相相位的峰值位置跟随时间增大;使用连续时间窗对运动中的锁相相位分布进行分析,结果表明锁相的相位分布在跟随时间变化,这种变化在CenterRelease事件发生前后更加剧烈,对相邻时间窗的均值差异分析表明伸手阶段内的锁相相位分布变化最为剧烈.对连续运动中不同的时间阶段锁相相位分布变化的分析所发现的规律,可以为解析大脑对运动过程中神经信息编码提供新的思路.

Altered Local Field Potential Relationship Between the Parafascicular Thalamic Nucleus and Dorsal Striatum in Hemiparkinsonian Rats

The thalamostriatal pathway is implicated in Parkinson's disease(PD); however, PD-related changes in the relationship between oscillatory activity in the centromedian-parafascicular complex(CM/Pf, or the Pf in rodents) and the dorsal striatum(DS) remain unclear.Therefore, we simultaneously recorded local field potentials(LFPs) in both the Pf and DS of hemiparkinsonian and control rats during epochs of rest or treadmill walking. The dopamine-lesioned rats showed increased LFP power in the beta band(12 Hz–35 Hz) in the Pf and DS during both epochs, but decreased LFP power in the delta(0.5 Hz–3 Hz) band in the Pf during rest epochs and in the DS during both epochs, compared to control rats. In addition,exaggerated low gamma(35 Hz–70 Hz) oscillations after dopamine loss were restricted to the Pf regardless of the behavioral state. Furthermore, enhanced synchronization of LFP oscillations was found between the Pf and DS after the dopamine lesion. Significant increases occurred in the mean coherence in both theta(3 Hz–7 Hz) and beta bands,and a significant increase was also noted in the phase coherence in the beta band between the Pf and DS during rest epochs. During the treadmill walking epochs, significant increases were found in both the alpha(7 Hz–12 Hz)and beta bands for two coherence measures. Collectively,dramatic changes in the relative LFP power and coherence in the thalamostriatal pathway may underlie the dysfunction of the basal ganglia-thalamocortical network circuits in PD, contributing to some of the motor and non-motor symptoms of the disease.

Novel Biological Based Method for Robot Navigation and Localization

The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.

Encoding of rat working memory by power of multi-channel local field potentials via sparse non-negative matrix factorization

Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multichannel local field potentials （LFPs） based on sparse non negative matrix factorization （SNMF）. SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four SpragueDawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the powerincreased LFP components were selected as working memoryrelated features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.

Mapping the Information Trace in Local Field Potentials by a Computational Method of Two-Dimensional Time-Shifting Synchronization Likelihood Based on Graphic Processing Unit Acceleration

The local field potential（LFP） is a signal reflecting the electrical activity of neurons surrounding the electrode tip. Synchronization between LFP signals provides important details about how neural networks are organized. Synchronization between two distant brain regions is hard to detect using linear synchronization algorithms like correlation and coherence. Synchronization likelihood（SL） is a non-linear synchronization-detecting algorithm widely used in studies of neural signals from two distant brain areas. One drawback of non-linear algorithms is the heavy computational burden. In the present study, we proposed a graphic processing unit（GPU）-accelerated implementation of an SL algorithm with optional 2-dimensional time-shifting. We tested the algorithm with both artificial data and raw LFP data. The results showed that this method revealed detailed information from original data with the synchronization values of two temporal axes,delay time and onset time, and thus can be used to reconstruct the temporal structure of a neural network. Our results suggest that this GPU-accelerated method can be extended to other algorithms for processing time-series signals（like EEG and f MRI） using similar recording techniques.