2,101 research outputs found

    Long-Term Potentiation in Isolated Dendritic Spines

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    BACKGROUND:In brain, N-methyl-D-aspartate (NMDA) receptor (NMDAR) activation can induce long-lasting changes in synaptic alpha-amino-3-hydroxy-5-methylisoxazole-4-propionate (AMPA) receptor (AMPAR) levels. These changes are believed to underlie the expression of several forms of synaptic plasticity, including long-term potentiation (LTP). Such plasticity is generally believed to reflect the regulated trafficking of AMPARs within dendritic spines. However, recent work suggests that the movement of molecules and organelles between the spine and the adjacent dendritic shaft can critically influence synaptic plasticity. To determine whether such movement is strictly required for plasticity, we have developed a novel system to examine AMPAR trafficking in brain synaptosomes, consisting of isolated and apposed pre- and postsynaptic elements. METHODOLOGY/PRINCIPAL FINDINGS:We report here that synaptosomes can undergo LTP-like plasticity in response to stimuli that mimic synaptic NMDAR activation. Indeed, KCl-evoked release of endogenous glutamate from presynaptic terminals, in the presence of the NMDAR co-agonist glycine, leads to a long-lasting increase in surface AMPAR levels, as measured by [(3)H]-AMPA binding; the increase is prevented by an NMDAR antagonist 2-amino-5-phosphonopentanoic acid (AP5). Importantly, we observe an increase in the levels of GluR1 and GluR2 AMPAR subunits in the postsynaptic density (PSD) fraction, without changes in total AMPAR levels, consistent with the trafficking of AMPARs from internal synaptosomal compartments into synaptic sites. This plasticity is reversible, as the application of AMPA after LTP depotentiates synaptosomes. Moreover, depotentiation requires proteasome-dependent protein degradation. CONCLUSIONS/SIGNIFICANCE:Together, the results indicate that the minimal machinery required for LTP is present and functions locally within isolated dendritic spines

    Electromagnetic Radiation Hardness of Diamond Detectors

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    The behavior of artificially grown CVD diamond films under intense electromagnetic radiation has been studied. The properties of irradiated diamond samples have been investigated using the method of thermally stimulated current and by studying their charge collection properties. Diamonds have been found to remain unaffected after doses of 6.8 MGy of 10 keV photons and 10 MGy of MeV-range photons. This observation makes diamond an attractive detector material for a calorimeter in the very forward region of the proposed TESLA detector.Comment: 19 pages, 9 figure

    A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models

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    This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (PMC), which is based on an iterative importance sampling approach. An important drawback of this methodology is the degeneracy of the importance weights when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a novel method that performs a nonlinear transformation on the importance weights. This operation reduces the weight variation, hence it avoids their degeneracy and increases the efficiency of the importance sampling scheme, specially when drawing from a proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a very simple problem that enables us to clearly show and discuss the main features of the proposed technique. As a practical application, we have also considered the popular (and challenging) problem of estimating the rate parameters of stochastic kinetic models (SKM). SKMs are highly multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.Comment: 35 pages, 8 figure

    On Instrumental Variable Regression for Deep Offline Policy Evaluation

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    We show that the popular reinforcement learning (RL) strategy of estimating the state-action value (Q-function) by minimizing the mean squared Bellman error leads to a regression problem with confounding, the inputs and output noise being correlated. Hence, direct minimization of the Bellman error can result in significantly biased Q-function estimates. We explain why fixing the target Q-network in Deep Q-Networks and Fitted Q Evaluation provides a way of overcoming this confounding, thus shedding new light on this popular but not well understood trick in the deep RL literature. An alternative approach to address confounding is to leverage techniques developed in the causality literature, notably instrumental variables (IV). We bring together here the literature on IV and RL by investigating whether IV approaches can lead to improved Q-function estimates. This paper analyzes and compares a wide range of recent IV methods in the context of offline policy evaluation (OPE), where the goal is to estimate the value of a policy using logged data only. By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques. We find empirically that state-of-the-art OPE methods are closely matched in performance by some IV methods such as AGMM, which were not developed for OPE

    On Reasoning on Time and Location on the Web

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    Reasoning on time and location is receiving increasing attention on the Web due to emerging fields like Web adaptation, mobile computing, and the Semantic Web. Web applications in these fields often refer to rather complex temporal, calendric, and location information. Unfortunately, today’s Web languages and formalisms have merely primitive temporal and location data types and temporal and location reasoning capabilities – if any. This article reports on work in progress aiming at integrating temporal and locational reasoning into XML query and transformation operations. We analyze the problem and propose a concrete architecture. A prototype of the temporal reasoner, the WebCal system has already been realized

    Adaptive Importance Sampling in General Mixture Classes

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    In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performances of the proposed scheme are studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.Comment: Removed misleading comment in Section

    Functional connectivity evidence of cortico-cortico inhibition in temporal lobe epilepsy.

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    Epileptic seizures can initiate a neural circuit and lead to aberrant neural communication with brain areas outside the epileptogenic region. We focus on interictal activity in focal temporal lobe epilepsy and evaluate functional connectivity (FC) differences that emerge as function of bilateral versus strictly unilateral epileptiform activity. We assess the strength of FC at rest between the ictal and non-ictal temporal lobes, in addition to whole brain connectivity with the ictal temporal lobe. Results revealed strong connectivity between the temporal lobes for both patient groups, but this did not vary as a function of unilateral versus bilateral interictal status. Both the left and right unilateral temporal lobe groups showed significant anti-correlated activity in regions outside the epileptogenic temporal lobe, primarily involving the contralateral (non-ictal/non-pathologic) hemisphere, with precuneus involvement prominent. The bilateral groups did not show this contralateral anti-correlated activity. This anti-correlated connectivity may represent a form of protective and adaptive inhibition, helping to constrain epileptiform activity to the pathologic temporal lobe. The absence of this activity in the bilateral groups may be indicative of flawed inhibitory mechanisms, helping to explain their more widespread epileptiform activity. Our data suggest that the location and build up of epilepsy networks in the brain are not truly random, and are not limited to the formation of strictly epileptogenic networks. Functional networks may develop to take advantage of the regulatory function of structures such as the precuneus to instantiate an anti-correlated network, generating protective cortico-cortico inhibition for the purpose of limiting seizure spread or epileptogenesis
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