[Show abstract][Hide abstract] ABSTRACT: The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.
[Show abstract][Hide abstract] ABSTRACT: The importance of the reliability of majority-based structures stems from their use in both conventional fault-tolerant architectures and emerging nanoelectronic systems. In this paper, analytical models are developed in order to gain a better understanding of the reliability of majority logic in these contexts. A minimally biased input scenario for N -input majority gates ( N odd) occurs when only a minimal majority of the inputs are in consensus. In a tree of gates with these inputs, this paper determines 1) that any nonzero error rate of the majority gates and/or of its initial inputs will result in an unreliable output and 2) that the use of majority gates with a larger number of inputs leads to a less reliable structure. These results are extended to N -input minority gates for odd N . Although these findings are based on tree structures, their implications to circuit design are explored by investigating several fault-tolerant and nanoelectronic architectures. The simulation results show that the increased probability of error in nanoscale devices may impose serious constraints on the reliability of emerging nanoelectronic circuits, as well as their fault-tolerant counterparts. The worst case reliability must be accounted for in a fault-tolerant design to ensure reliable operation.
[Show abstract][Hide abstract] ABSTRACT: J. Neurochem. (2011) 118, 784–795.
Curcumin, a major active component of Curcuma longa, possesses antioxidant and neuroprotective activities. The present study explores the mechanisms underlying the neuroprotective effect of curcumin against corticosterone and its relation to 5-hydroxy tryptamine (5-HT) receptors. Exposure of cortical neurons to corticosterone results in decreased mRNA levels for three 5-HT receptor subtypes, 5-HT1A, 5-HT2A and 5-HT4, but 5-HT1B, 5-HT2B, 5-HT2C, 5-HT6 and 5-HT7 receptors remain unchanged. Pre-treatment with curcumin reversed this effect on mRNA for the 5-HT1A and 5-HT4 receptors, but not for the 5-HT2A receptor. Moreover, curcumin exerted a neuroprotective effect against corticosterone-induced neuronal death. This observed effect of curcumin was partially blocked by either 5-HT1A receptor antagonist p-MPPI or 5-HT4 receptor antagonist RS 39604 alone; whereas, the simultaneous application of both antagonists completely reversed the effect. Curcumin was also found to regulate corticosterone-induced morphological changes such as increases in soma size, dendritic branching and dendritic spine density, as well as elevate synaptophysin expression in cortical neurons. p-MPPI and RS 39604 reversed the effect of curcumin-induced change in neuronal morphology and synaptophysin expression of corticosterone-treated neurons. In addition, an increase in cyclic adenosine monophosphate (cAMP) level was observed after curcumin treatment, which was further prevented by RS 39604, but not by p-MPPI. However, curcumin-induced elevation in protein kinase A activity and phosphorylation of cAMP response element-binding protein levels were inhibited by both p-MPPI and RS 39604. These findings suggest that the neuroprotection and modulation of neuroplasticity exhibited by curcumin might be mediated, at least in part, via the 5-HT receptor-cAMP-PKA-CREB signal pathway.
Journal of Neurochemistry 06/2011; 118(5):784-95. DOI:10.1111/j.1471-4159.2011.07356.x · 4.28 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Stress is an unavoidable life experience. It induces mood, cognitive dysfunction and plasticity changes in chronically stressed individuals. Among the various brain regions that have been studied, the hippocampus and amygdala have been observed to have different roles in controlling the limbic-hypothalamic-pituitary-adrenal axis (limbic-HPA axis). This study investigated how the stress hormone corticosterone (CORT) affects neuronal cells. The first aim is to test whether administration of CORT to hippocampal and amygdaloid cell lines induces different changes in the 5-HT receptor subtypes. The second goal is to determine whether stress induced morphological changes in these two cell lines were involved in the 5-HT receptor subtypes expression. We now show that 5-HT(7) receptor mRNA levels were significantly upregulated in HT-22 cells, but downregulated in AR-5 cells by exposure to a physiologically relevant level of CORT (50 μM) for 24 h, which was later confirmed by primary hippocampal and amygdaloid neuron cultures. Additionally, pretreatment of cells with 5-HT(7) antagonist SB-269970 or agonist LP-44 reversed CORT induced cell lesion in a dose-dependent manner. Moreover, CORT induced different changes in neurite length, number of neurites and soma size in HT-22 and AR-5 cells were also reversed by pretreatment with either SB-269970 or LP-44. The different effects of 5-HT(7) receptors on cell lines were observed in two members of the Rho family small GTPase expression: the Cdc-42 and RhoA. These observed results support the hypothesis that 5-HT may differentially modulate neuronal morphology in the hippocampus and amygdala depending on the expression levels of the 5-HT receptor subtypes during stress hormone insults.
[Show abstract][Hide abstract] ABSTRACT: Logic circuits built using nanoscale technologies have significant reliability limitations due to fundamental physical and manufacturing constraints of their constituent devices. This paper presents a probabilistic gate model (PGM), which relates the output probability to the error and input probabilities of an unreliable logic gate. The PGM is used to obtain computational algorithms, one being approximate and the other accurate, for the evaluation of circuit reliability. The complexity of the approximate algorithm, which does not consider dependencies among signals, increases linearly with the number of gates in a circuit. The accurate algorithm, which accounts for signal dependencies due to reconvergent fanouts and/or correlated inputs, has a worst-case complexity that is exponential in the numbers of dependent reconvergent fanouts and correlated inputs. By leveraging the fact that many large circuits consist of common logic modules, a modular approach that hierarchically decomposes a circuit into smaller modules and subsequently applies the accurate PGM algorithm to each module, is further proposed. Simulation results are presented for applications on the LGSynth91 and ISCAS85 benchmark circuits. It is shown that the modular PGM approach provides highly accurate results with a moderate computational complexity. It can further be embedded into an early design flow and is scalable for use in the reliability evaluation of large circuits.
[Show abstract][Hide abstract] ABSTRACT: Experimental data from biological pathways come in many forms: qualitative or quantitative, static or dynamic. By combining a variety of these heterogeneous sources of data, we construct a mathematical model of a critical regulatory network in vertebrate development, the Sonic Hedgehog signaling pathway. The structure of our model is first constrained by several well-established pathway interactions. On top of this, we develop a hierarchical genetic algorithm that is capable of integrating different types of experimental data collected on the pathway's function, including qualitative as well as static and dynamic quantitative data, in order to estimate model parameters. The result is a dynamical model that fits the observed data and is robust to perturbations in its parameters. Since it is based on a canonical power-law representation of biochemical pathways whose parameters can be directly translated into physical interactions between network components, our model provides insight into the nature and strength of pathway interactions and suggests directions for future research.