Effects of Naotan Pill on repair of neural cells and cognitive disorders in juvenile rats following hypoxia and ischemia

BACKGROUND: Hypoxia and ischemia induce neuronal damage, decreased neuronal numbers and synaptophysin levels, and deficits in learning and memory functions. Previous studies have shown that lycium barbarum polysaccharide, the most effective component of barbary wolfberry fruit, has protective effects on neural cells in hypoxia-ischemia. OBJECTIVE: To investigate the effects of Naotan Pill on glutamate-treated neural cells and on cognitive function in juvenile rats following hypoxia-ischemia. DESIGN, TIME AND SETTING: The randomized, controlled, in vivo study was performed at the Cell Laboratory of Lanzhou University, Lanzhou Institute of Modern Physics of Chinese Academy of Sciences, and Department of Traditional Chinese Medicine of Gansu Provincial Rehabilitation Center Hospital, China from December 2005 to August 2006. The cellular neurobiology, in vitro experiment was conducted at the Institute of Human Anatomy, Histology, Embryology and Neuroscience, School of Basic Medical Sciences, Lanzhou University, and Department of Traditional Chinese Medicine of Gansu Provincial Rehabilitation Center Hospital, China from March 2007 to January 2008. MATERIALS: Naotan Pill, composed of barbary wolfberry fruit, danshen root, grassleaf sweetflag rhizome, and glossy privet fruit, was prepared by Gansu Provincial Rehabilitation Center, China. Rabbit anti-synaptophysin, choline acetyl transferase polyclonal antibody, streptavidin-biotin complex kit and diaminobenzidine kit (Boster, Wuhan, China), as well as glutamate (Hualian, Shanghai, China) were used in this study. METHODS: Cortical neural cells were isolated from neonatal Wistar rats. Neural cell damage models were induced using glutamate, and administered Naotan Pill prior to and following damage. A total of 54 juvenile Wistar rats were equally and randomly assigned into model, Naotan Pill, and sham operation groups. The left common carotid artery was ligated, and then rat models of hypoxic-ischemic injury were assigned to the model and Naotan Pill groups. At 2 days following model induction, rats in the Naotan Pill group were administered Naotan Pill suspension for 21 days. In the model and sham operation groups, rats received an equal volume of saline. MAIN OUTCOME MEASURES: Neural cell morphology was observed using an inverted phase contrast microscope. Survival rate of neural cells was measured by MTT assay. Synaptophysin and choline acetyl transferase expression was observed in the hippocampal CA1 region of juvenile rats using immunohistochemistry. Cognitive function was tested by the Morris water maze. RESULTS: Pathological changes were detected in glutamate-treated neural cells. Neural cell morphology remained normal after Naotan Pill intervention. Absorbance and survival rate of neural cells were significantly greater following Naotan Pill intervention, compared to glutamate-treated neural cells (P < 0.05). Synaptophysin and choline acetyl transferase expression was lowest in the hippocampal CA1 region in the model group and highest in the sham operation group. Significant differences among groups were observed (P < 0.05). Escape latency and swimming distance were significantly longer in the model group compared to the Naotan Pill group (P < 0.05). CONCLUSION: Naotan Pill exhibited protective and repair effects on glutamate-treated neural cells. Naotan Pill upregulated synaptophysin and choline acetyl transferase expression in the hippocampus and improved cognitive function in rats following hypoxia-ischemia.

Origin of negative differential resistance and memory characteristics in organic devices based on tris(8-hydroxyquinoline) aluminum

Negative differential resistance (NDR) and memory phenomenon have been realized in current-voltage (I-V) characteristics of indium tin oxide/tris(8-hydroxyquinoline) aluminum/aluminum devices. The I-V curves have been divided into three operational regions that are associated with different working regimes of the devices: (i) bistable region, (ii) NDR region, and (iii) monotonic region. The bistable region disappeared after a couple of voltage sweeps from zero to a positive voltage. The bistable nature can be reinstated by applying a suitable negative voltage.

Negative differential resistance and memory effect in diodes based on 1,4-dibenzyl C60 and zinc phthalocyanine doped polystyrene hybrid material

Negative differential resistance (NDR) and memory effect were observed in diodes based on 1,4-dibenzyl C60 (DBC) and zinc phthalocyanine doped polystyrene hybrid material. Certain negative starting sweeping voltages led to a reproducible NDR, making the hybrid material a promising candidate in memory devices. It was found that the introduction of DBC enhanced the ON/OFF current ratio and significantly improved the memory stability. The ON/OFF current ratio was up to 2 orders of magnitude. The write-read-erase-reread cycles were more than 10(6), and the retention time reached 10 000 s without current degradation.

Leveraging Learning and Language Via Communication Bootstrapping

In a Communication Bootstrapping system, peer components with different perceptual worlds invent symbols and syntax based on correlations between their percepts. I propose that Communication Bootstrapping can also be used to acquire functional definitions of words and causal reasoning knowledge. I illustrate this point with several examples, then sketch the architecture of a system in progress which attempts to execute this task.

Learning and Example Selection for Object and Pattern Detection

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.

Workplace learning and the employment relationship in the public sector.

Training that is relevant to employers is not necessarily enriching for employees, especially those on the lower salary scales. The authors argue that the analysis of training and development needs to be understood in the context of the employment relationship. Drawing on reasearch evidence from six case studies in the public sector, the article examines the impact of changes in work organisation on workplace learning, managers' and employees' own strategies towards it and the limitations of tools such as appraisal. Since employees' existing qualifications are poorly utilised and their development needs often frustrated, issues concerning job design, occupational progression routes and employee entitlements need to be addressed

Learning and Control in Assistive Robotics for the Elderly

Q. Meng and M. H. Lee, Learning and Control in Assistive Robotics for the Elderly, IEEE Conference on Robotics, Automation and Mechatronics (RAM), Singapore, 2004.

Learning and Reuse of Experience in Behavior-Based Service Robots

M.H.Lee, Q. Meng and H. Holstein, ?Learning and Reuse of Experience in Behavior-Based Service Robots?, Seventh International Conference on Control, Automation, Robotics and Vision (ICARCV2002), pp1019-24, December 2002, Singapore

Brain Learning and Recognition: The Large and the Small of It in Inferotemporal Cortex

Anterior inferotemporal cortex (ITa) plays a key role in visual object recognition. Recognition is tolerant to object position, size, and view changes, yet recent neurophysiological data show ITa cells with high object selectivity often have low position tolerance, and vice versa. A neural model learns to simulate both this tradeoff and ITa responses to image morphs using large-scale and small-scale IT cells whose population properties may support invariant recognition.

Neural Dynamics of Motion Perception, Recognition Learning, and Spatial Attention

Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-24877); Office of Naval Research (N00014-91-J-4100)

Fuzzy ARTMAP, Slow Learning and Probability Estimation

A nonparametric probability estimation procedure using the fuzzy ARTMAP neural network is here described. Because the procedure does not make a priori assumptions about underlying probability distributions, it yields accurate estimates on a wide variety of prediction tasks. Fuzzy ARTMAP is used to perform probability estimation in two different modes. In a 'slow-learning' mode, input-output associations change slowly, with the strength of each association computing a conditional probability estimate. In 'max-nodes' mode, a fixed number of categories are coded during an initial fast learning interval, and weights are then tuned by slow learning. Simulations illustrate system performance on tasks in which various numbers of clusters in the set of input vectors mapped to a given class.

Simulating Effects of Learning and Lesions with a Model of Intrinsic and Synaptically Gated Responses of Striatal Cholinergic Interneurons

The giant cholinergic interneurons of the striatum are tonically active neurons (TANs) that respond with characteristic pauses to novel events and to appetitive and aversive conditioned stimuli. Fluctuations in acetylcholine release by TANs modulate performance- and learning-related dynamics in the striatum. Whereas tonic activity emerges from intrinsic properties of these neurons, glutamatergic inputs from thalamic centromedian-parafascicular nuclei, and dopaminergic inputs from midbrain, are required for the generation of pause responses. No prior computational models encompass both intrinsic and synaptically-gated dynamics. We present a mathematical model that robustly accounts for behavior-related electrophysiological properties of TANs in terms of their intrinsic physiological properties and known afferents. In the model, balanced intrinsic hyperpolarizing and depolarizing currents engender tonic firing, and glutamatergic inputs from thalamus (and cortex) both directly excite and indirectly inhibit TANs. If the latter inhibition, presumably mediated by GABAergic interneurons, exceeds a threshold, its effect is amplified by a KIR current to generate a prolonged pause. In the model, the intrinsic mechanisms and external inputs are both modulated by learning-dependent dopamine (DA) signals and our simulations revealed that many learning-dependent behaviors of TANs are explicable without recourse to learning-dependent changes in synapses onto TANs. The "teaching signal" that modulates reinforcement learning at cortico-striatal synapses may be a sequence composed of an adaptively scaled DA burst, a brief ACh burst, and a scaled ACh pause. Such an interpretation is consistent with recent data on cholinergic control of LTD of cortical synapses onto striatal spiny projection neurons.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

A Neural Network of Adaptively Timed Reinforcement Learning and Hippocampal Dynamics

A neural model is described of how adaptively timed reinforcement learning occurs. The adaptive timing circuit is suggested to exist in the hippocampus, and to involve convergence of dentate granule cells on CA3 pyramidal cells, and NMDA receptors. This circuit forms part of a model neural system for the coordinated control of recognition learning, reinforcement learning, and motor learning, whose properties clarify how an animal can learn to acquire a delayed reward. Behavioral and neural data are summarized in support of each processing stage of the system. The relevant anatomical sites are in thalamus, neocortex, hippocampus, hypothalamus, amygdala, and cerebellum. Cerebellar influences on motor learning are distinguished from hippocampal influences on adaptive timing of reinforcement learning. The model simulates how damage to the hippocampal formation disrupts adaptive timing, eliminates attentional blocking, and causes symptoms of medial temporal amnesia. It suggests how normal acquisition of subcortical emotional conditioning can occur after cortical ablation, even though extinction of emotional conditioning is retarded by cortical ablation. The model simulates how increasing the duration of an unconditioned stimulus increases the amplitude of emotional conditioning, but does not change adaptive timing; and how an increase in the intensity of a conditioned stimulus "speeds up the clock", but an increase in the intensity of an unconditioned stimulus does not. Computer simulations of the model fit parametric conditioning data, including a Weber law property and an inverted U property. Both primary and secondary adaptively timed conditioning are simulated, as are data concerning conditioning using multiple interstimulus intervals (ISIs), gradually or abruptly changing ISis, partial reinforcement, and multiple stimuli that lead to time-averaging of responses. Neurobiologically testable predictions are made to facilitate further tests of the model.

Leveraging lessons learned in organizations through implementing practice-based organizational learning and performance improvement - An opportunity for context-based intelligent assistant support (CIAS)

Organizations that leverage lessons learned from their experience in the practice of complex real-world activities are faced with five difficult problems. First, how to represent the learning situation in a recognizable way. Second, how to represent what was actually done in terms of repeatable actions. Third, how to assess performance taking account of the particular circumstances. Fourth, how to abstract lessons learned that are re-usable on future occasions. Fifth, how to determine whether to pursue practice maturity or strategic relevance of activities. Here, organizational learning and performance improvement are investigated in a field study using the Context-based Intelligent Assistant Support (CIAS) approach. A new conceptual framework for practice-based organizational learning and performance improvement is presented that supports researchers and practitioners address the problems evoked and contributes to a practice-based approach to activity management. The novelty of the research lies in the simultaneous study of the different levels involved in the activity. Route selection in light rail infrastructure projects involves practices at both the strategic and operational levels; it is part managerial/political and part engineering. Aspectual comparison of practices represented in Contextual Graphs constitutes a new approach to the selection of Key Performance Indicators (KPIs). This approach is free from causality assumptions and forms the basis of a new approach to practice-based organizational learning and performance improvement. The evolution of practices in contextual graphs is shown to be an objective and measurable expression of organizational learning. This diachronic representation is interpreted using a practice-based organizational learning novelty typology. This dissertation shows how lessons learned when effectively leveraged by an organization lead to practice maturity. The practice maturity level of an activity in combination with an assessment of an activity’s strategic relevance can be used by management to prioritize improvement effort.