Dose-dependent effects of the dopamine D1 receptor agonists A77636 or SKF81297 on spatial working memory in aged monkeys

With advancing age, monkeys develop deficits in spatial working memory resembling those induced by lesions of the prefrontal cortex (PFC). Aged monkeys also exhibit marked loss of dopamine from the PFC, a transmitter known to be important for proper PFC cognitive function. Previous results suggest that D1 agonist treatment can improve spatial working memory abilities in aged monkeys. However, this research was limited by the use of drugs with either partial agonist actions or significant D2 receptor actions. In our study, the selective dopamine D1 receptor full agonists A77636 and SKF81297 were examined in aged monkeys for effects on the working memory functions of the PFC. Both compounds produced a significant, dose-related effect on delayed response performance without evidence of side effects: low doses improved performance although higher doses impaired or had no effect on performance. Both the improvement and impairment in performance were reversed by pretreatment with the D1 receptor antagonist, SCH23390. These findings are consistent with previous results demonstrating that there is a narrow range of D1 receptor stimulation for optimal PFC cognitive function, and suggest that very low doses of D1 receptor agonists may have cognitive-enhancing actions in the elderly.

Temperature dependent electron transport in amorphous oxide semiconductor thin film transistors

A temperature-dependent mobility model in amorphous oxide semiconductor (AOS) thin film transistors (TFTs) extracted from measurements of source-drain terminal currents at different gate voltages and temperatures is presented. At low gate voltages, trap-limited conduction prevails for a broad range of temperatures, whereas variable range hopping becomes dominant at lower temperatures. At high gate voltages and for all temperatures, percolation conduction comes into the picture. In all cases, the temperature-dependent mobility model obeys a universal power law as a function of gate voltage. © 2011 IEEE.

PH-dependent leaching behaviour and other performance properties of cement-treated mixed contaminated soil

Portland cement has been widely used for stabilisation/solidification (S/S) treatment of contaminated soils. However, there is a dearth of literature on pH-dependent leaching of contaminants from cement-treated soils. This study investigates the leachability of Cu, Pb, Ni, Zn and total petroleum hydrocarbons (TPH) from a mixed contaminated soil. A sandy soil was spiked with 3000 mg/kg each of Cd, Cu, Pb, Ni and Zn, and 10,000 mg/kg of diesel, and treated with ordinary Portland cement (CEM I). Four different binder dosages, 5%, 10%, 15% and 20% (m/m) and different water contents ranging from 13%-19% dry weight were used in order to find a safe operating envelope for the treatment process. The pH-dependent leaching behaviour of the treated soil was monitored over an 84-day period using a 3-point acid neutralisation capacity (ANC) test. The monolithic leaching test was also conducted. Geotechnical properties such as unconfined compressive strength (UCS), hydraulic conductivity and porosity were assessed over time. The treated soils recorded lower leachate concentrations of Ni and Zn compared to the untreated soil at the same pH depending on binder dosage. The binder had problems with Pb stabilisation and TPH leachability was independent of pH and binder dosage. The hydraulic conductivity of the mixes was generally of the order, 10-8 m/sec, while the porosity ranged from 26%-44%. The results of selected performance properties are compared with regulatory limits and the range of operating variables that lead to acceptable performance described. © 2012 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences.

New application of temperature-dependent modelling of high temperature superconductors: Quench propagation and pulse magnetization

We present temperature-dependent modeling of high-temperature superconductors (HTS) to understand HTS electromagnetic phenomena where temperature fluctuation plays a nontrivial role. Thermal physics is introduced into the well-developed H-formulation model, and the effect of temperature-dependent parameters is considered. Based on the model, we perform extensive studies on two important HTS applications: quench propagation and pulse magnetization. A micrometer-scale quench model of HTS coil is developed, which can be used to estimate minimum quench energy and normal zone propagation velocity inside the coil. In addition, we study the influence of inhomogeneity of HTS bulk during pulse magnetization. We demonstrate how the inhomogeneous distribution of critical current inside the bulk results in varying degrees of heat dissipation and uniformity of final trapped field. The temperature- dependent model is proven to be a powerful tool to study the thermally coupled electromagnetic phenomena of HTS. © 2012 American Institute of Physics.

A dependent partition-valued process for multitask clustering and time evolving network modelling

The fundamental aim of clustering algorithms is to partition data points. We consider tasks where the discovered partition is allowed to vary with some covariate such as space or time. One approach would be to use fragmentation-coagulation processes, but these, being Markov processes, are restricted to linear or tree structured covariate spaces. We define a partition-valued process on an arbitrary covariate space using Gaussian processes. We use the process to construct a multitask clustering model which partitions datapoints in a similar way across multiple data sources, and a time series model of network data which allows cluster assignments to vary over time. We describe sampling algorithms for inference and apply our method to defining cancer subtypes based on different types of cellular characteristics, finding regulatory modules from gene expression data from multiple human populations, and discovering time varying community structure in a social network.

Spin glass behaviour and spin-dependent scattering in la 0.7Ca 0.3Mn 0.9Cr 0.1O 3 perovskites

The magnetic, electrical and thermal transport properties of the perovskite La 0.7Ca 0.3Mn 0.9Cr 0.1O 3 have been investigated by measuring dc magnetization, ac susceptibility, the magnetoresistance and thermal conductivity in the temperature range of 5-300K. The spin glass behaviour with a spin freezing temperature of 70 K has been well confirmed for this compound, which demonstrates the coexistence and competition between ferromagnetic and antiferromagnetic clusters by the introduction of Cr. Colossal magnetoresistance has been observed over the temperature range investigated. The introduction of Cr causes the "double-bump" feature in electrical resistivity ρ(T). Anomalies on the susceptibility and the thermal conductivity associated with the double-bumps in ρ(T) are observed simultaneously. The imaginary part of ac susceptibility shows a sharp peak at the temperature of insulating-metallic transition where the first resistivity bump was observed, but it is a deep-set valley near the temperature where the second bump in ρ(T) emerges. The thermal conductivity shows an increase below the temperature of the insulating-metallic transition, but the phonon scattering is enhanced accompanying the appearance of the second peak of double-bumps in ρ(T). We relate those observed in magnetic and transport properties of La 0.7Ca 0.3Mn 0.9Cr 0.1O 3 to the spin-dependent scattering. The results reveal that the spin-phonon interaction may be of more significance than the electron (charge)-phonon interaction in the mixed perovskite system. © 2005 Chinese Physical Society and IOP Publishing Ltd.

Context-dependent human extinction memory is mediated by a ventromedial prefrontal and hippocampal network.

In fear extinction, an animal learns that a conditioned stimulus (CS) no longer predicts a noxious stimulus [unconditioned stimulus (UCS)] to which it had previously been associated, leading to inhibition of the conditioned response (CR). Extinction creates a new CS-noUCS memory trace, competing with the initial fear (CS-UCS) memory. Recall of extinction memory and, hence, CR inhibition at later CS encounters is facilitated by contextual stimuli present during extinction training. In line with theoretical predictions derived from animal studies, we show that, after extinction, a CS-evoked engagement of human ventromedial prefrontal cortex (VMPFC) and hippocampus is context dependent, being expressed in an extinction, but not a conditioning, context. Likewise, a positive correlation between VMPFC and hippocampal activity is extinction context dependent. Thus, a VMPFC-hippocampal network provides for context-dependent recall of human extinction memory, consistent with a view that hippocampus confers context dependence on VMPFC.

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans.

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

Functional requirements for reward-modulated spike-timing-dependent plasticity.

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity is conditioned on a global modulatory factor signaling reward. We show that all learning rules in this class can be separated into a term that captures the covariance of neuronal firing and reward and a second term that presents the influence of unsupervised learning. The unsupervised term, which is, in general, detrimental for reward-based learning, can be suppressed if the neuromodulatory signal encodes the difference between the reward and the expected reward-but only if the expected reward is calculated for each task and stimulus separately. If several tasks are to be learned simultaneously, the nervous system needs an internal critic that is able to predict the expected reward for arbitrary stimuli. We show that, with a critic, reward-modulated spike-timing-dependent plasticity is capable of learning motor trajectories with a temporal resolution of tens of milliseconds. The relation to temporal difference learning, the relevance of block-based learning paradigms, and the limitations of learning with a critic are discussed.

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.