[Reward information encoded by power of local field potentials in the primate prefrontal cortex and striatum].

Prefrontal cortex and striatum are two major areas in the brain. Some research reports suggest that both areas are involved in many advanced cognitive processes, such as learning and memory, reward processing, and behavioral decision. Single-unit recording experiments have found that neurons in the prefrontal cortex and striatum can represent reward information, but it remains elusive whether and how local field potentials (LFPs) in the two areas encode reward information. To investigate these issues, we recorded LFPs simultaneously in the prefrontal cortex and striatum of two monkeys by performing a reward prediction task (a large amount reward vs a small amount reward). Recorded LFP signals were transformed from the time domain to the time and frequency domain using the method of short-time Fourier transform (STFT). We calculated the power in each frequency and time, and examined whether they were different in the two reward conditions. The results showed that power of LFPs in both the prefrontal cortex and striatum distinguished one reward condition from the other one. And the power in small reward trials was greater than that in large reward trials. Furthermore, it was found that the LFPs better encoded reward information in the beta band (14-30 Hz) rather than other frequency bands. Our results suggest that the LFPs in the prefrontal cortex and striatum effectively represent reward information, which would help to further understand functional roles of LFPs in reward processing.

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.

BACKGROUND AND OBJECTIVE: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. METHODS: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. RESULTS: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). CONCLUSION: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller.

Visual pattern discrimination by population retinal ganglion cells' activities during natural movie stimulation.

In the visual system, neurons often fire in synchrony, and it is believed that synchronous activities of group neurons are more efficient than single cell response in transmitting neural signals to down-stream neurons. However, whether dynamic natural stimuli are encoded by dynamic spatiotemporal firing patterns of synchronous group neurons still needs to be investigated. In this paper we recorded the activities of population ganglion cells in bullfrog retina in response to time-varying natural images (natural scene movie) using multi-electrode arrays. In response to some different brief section pairs of the movie, synchronous groups of retinal ganglion cells (RGCs) fired with similar but different spike events. We attempted to discriminate the movie sections based on temporal firing patterns of single cells and spatiotemporal firing patterns of the synchronous groups of RGCs characterized by a measurement of subsequence distribution discrepancy. The discrimination performance was assessed by a classification method based on Support Vector Machines. Our results show that different movie sections of the natural movie elicited reliable dynamic spatiotemporal activity patterns of the synchronous RGCs, which are more efficient in discriminating different movie sections than the temporal patterns of the single cells' spike events. These results suggest that, during natural vision, the down-stream neurons may decode the visual information from the dynamic spatiotemporal patterns of the synchronous group of RGCs' activities.

Predictive modeling of human operator cognitive state via sparse and robust support vector machines.

The accurate prediction of the temporal variations in human operator cognitive state (HCS) is of great practical importance in many real-world safety-critical situations. However, since the relationship between the HCS and electrophysiological responses of the operator is basically unknown, complicated and uncertain, only data-based modeling method can be employed. This paper is aimed at constructing a data-driven computationally intelligent model, based on multiple psychophysiological and performance measures, to accurately estimate the HCS in the context of a safety-critical human-machine system. The advanced least squares support vector machines (LS-SVM), whose parameters are optimized by grid search and cross-validation techniques, are adopted for the purpose of predictive modeling of the HCS. The sparse and weighted LS-SVM (WLS-SVM) were proposed by Suykens et al. to overcome the deficiency of the standard LS-SVM in lacking sparseness and robustness. This paper adopted those two improved LS-SVM algorithms to model the HCS based solely on a set of physiological and operator performance data. The results showed that the sparse LS-SVM can obtain HCS models with sparseness with almost no loss of modeling accuracy, while the WLS-SVM leads to models which are robust in case of noisy training data. Both intelligent system modeling approaches are shown to be capable of capturing the temporal fluctuation trends of the HCS because of their superior generalization performance.

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

Effects of diacetyl guan-fu base A on pacemaker cells in sinoatrial node of rabbits.

AIM: To study the electrophysiological effects of diacetyl guan-fu base A (DGFA) on pacemaker cells in sinoatrial (SA) node. METHODS: Intracellular microelectrode method was used to record parameters of action potential (AP) in SA node of rabbits. RESULTS: DGFA could not only slow down spontaneous firing frequency (SFF), mean rate of repolarization (MRR), and rate of diastolic depolarization (RDD), but also prolong diastolic interval (DI) and duration of action potential (APD) in a concentration-dependent manner in SA node. Furthermore, DGFA markedly decreased the maximum rate of depolarization (MRD) with a slight reduce of the amplitude of action potential (APA) and there was no significant effect on the maximal diastolic potential (MDP). The decrease in SFF caused by DGFA was not affected by atropine (0.05 mg/L). CONCLUSION: The effects might be due to the reduction of calcium influx and potassium efflux, and the muscarinic receptors were not involved.