Improving Associative Memory in a Network of Spiking Neurons

In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition.We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory

Stabilization of colloidal suspensions by means of highly-charged nanoparticles

We employ a novel Monte Carlo simulation scheme to elucidate the stabilization of neutral colloidal microspheres by means of highly-charged nanoparticles [V. Tohver et al., Proc. Natl. Acad. Sci. U.S.A. 98, 8950 (2001)]. In accordance with the experimental observations, we find that small nanoparticle concentrations induce an effective repulsion that prevents gelation caused by the intrinsic van der Waals attraction between colloids. Higher nanoparticle concentrations induce an attractive potential which is, however, qualitatively different from the regular depletion attraction. We also show how colloid-nanoparticle size asymmetry and nanoparticle charge can be used to manipulate the effective interactions.Comment: Accepted for publication in Physical Review Letters. See also S. Karanikas and A.A. Louis, cond-mat/0411279. Updated to synchronize with published versio

IMPLEMENTATION OF A PRE-ANESTHESIA TAKE-HOME EVALUATION (PATHE) AS A MEANS OF PROCESS IMPROVEMENT: A BEST PRACTICE RECOMMENDATION

The pre-anesthesia evaluation (PAE) is a vital component to anesthesia providers when choosing an appropriate anesthetic plan for patients requiring surgery. The PAE, pre-anesthesia risk-factor assessment, and provision of pre-anesthesia instructions are standards of care implemented in order to assess the patient’s likely outcome of surgery and anesthesia as well as stratify any known risk factors to optimize surgical/anesthetic outcomes. Any disruption in this process could potentially lead to decreased patient/provider satisfaction, reduced patient compliance with pre-anesthetic instructions, reduced patient safety, and unnecessary financial burden. After the completion of a literature review, the need for a best practice recommendation was identified and a document was created containing a Pre-Anesthetic Take-Home Evaluation (PATHE). PATHE specifically aims to improve the pre-anesthesia assessment process through increased patient reporting of pertinent health history, stratification of pertinent risk factors, and pre-anesthesia education. The PATHE document was provided to seven practicing certified registered nurse anesthetists (CRNAs) and 22 student registered nurse anesthetists (SRNAs) currently in clinical. Of the respondents, 100% agreed that the document was thorough, well organized, and free from grammatical and formatting errors. Twenty-eight respondents (96.55%) agreed that the document would be easy for adults (age \u3e 18 years) of all cognitive levels to comprehend; however, one respondent (3.45%) disagreed. Additionally, 100% of respondents agreed that the document provides a clear representation of all majors aspects of anesthesia care, addresses most commonly encountered questions from patients, provides an accurate depiction of all topics addressed, addresses most commonly encountered risks associated with anesthesia, solicits the minimal amount of information required to develop a safe and effective plan for anesthesia care, provides a clear and accurate list of risks, and provides recommendations for risk stratification that are supported by current evidence. Lastly, one constructive comment left by a respondent stated that the document was too long, which could potentially deter patient compliance. With consideration of the literature review and survey results, the authors have concluded that patients, healthcare providers, nurses, and healthcare facilities all stand to benefit from the implementation of PATHE into their current evidence-based practice

Does mental well-being protect against self-harm thoughts and behaviors during adolescence? A six-month prospective investigation

Mental well-being protects against the emergence of suicidal thoughts. However, it is not clear whether these findings extend to self-harm thoughts and behaviors irrespective of intent during adolescence—or why this relationship exists. The current study aimed to test predictions—informed by the integrated motivational–volitional (IMV) model of suicide—concerning the role of perceived defeat and entrapment within the link between mental well-being and self-harm risk. Young people (n = 573) from secondary schools across Scotland completed an anonymous self-report survey at two time points, six months apart, that assessed mental well-being, self-harm thoughts and behaviors, depressive symptomology and feelings of defeat and entrapment. Mental well-being was associated with reduced defeat and entrapment (internal and external) and a decrease in the likelihood that a young person would engage in self-harm thoughts and behaviors. The relationship between mental well-being and thoughts of self-harm was mediated by perceptions of defeat and entrapment (internal and external). Mental well-being was indirectly related to self-harm behaviors via decreased feelings of defeat and internal (but not external) entrapment. Taken together, these findings provide novel insights into the psychological processes linking mental well-being and self-harm risk and highlight the importance of incorporating the promotion of mental well-being within future prevention and early intervention efforts

Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem

<jats:p>In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape’s ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.</jats:p&gt

Colloidal stabilization via nanoparticle haloing

We present a detailed numerical study of effective interactions between micron-sized silica spheres, induced by highly charged zirconia nanoparticles. It is demonstrated that the effective interactions are consistent with a recently discovered mechanism for colloidal stabilization. In accordance with the experimental observations, small nanoparticle concentrations induce an effective repulsion that counteracts the intrinsic van der Waals attraction between the colloids and thus stabilizes the suspension. At higher nanoparticle concentrations an attractive potential is recovered, resulting in reentrant gelation. Monte Carlo simulations of this highly size-asymmetric mixture are made possible by means of a geometric cluster Monte Carlo algorithm. A comparison is made to results obtained from the Ornstein-Zernike equations with the hypernetted-chain closure

Large Attractive Depletion Interactions in Soft Repulsive-Sphere Binary Mixtures

We consider binary mixtures of soft repulsive spherical particles and calculate the depletion interaction between two big spheres mediated by the fluid of small spheres, using different theoretical and simulation methods. The validity of the theoretical approach, a virial expansion in terms of the density of the small spheres, is checked against simulation results. Attention is given to the approach toward the hard-sphere limit, and to the effect of density and temperature on the strength of the depletion potential. Our results indicate, surprisingly, that even a modest degree of softness in the pair potential governing the direct interactions between the particles may lead to a significantly more attractive total effective potential for the big spheres than in the hard-sphere case. This might lead to significant differences in phase behavior, structure and dynamics of a binary mixture of soft repulsive spheres. In particular, a perturbative scheme is applied to predict the phase diagram of an effective system of big spheres interacting via depletion forces for a size ratio of small and big spheres of 0.2; this diagram includes the usual fluid-solid transition but, in the soft-sphere case, the metastable fluid-fluid transition, which is probably absent in hard-sphere mixtures, is close to being stable with respect to direct fluid-solid coexistence. From these results the interesting possibility arises that, for sufficiently soft repulsive particles, this phase transition could become stable. Possible implications for the phase behavior of real colloidal dispersions are discussed.Comment: 31 pages, 8 figures; version accepted for publication in the Journal of Chemical Physic

Gestational Vulnerability to Ozone Air Pollution - A Placental Story

About 99% of the global population resides in areas with air pollution surpassing World Health Organization standards. Air pollution is associated with adverse neonatal health outcomes such as low fetal birth weight and an increased risk for maternal pre-eclampsia. A particularly reactive air pollutant is ozone, which forms reactive oxygen species that induce cellular damage. Research exists on the dispersion of reactive oxygen species through the bloodstream leading to fetal vulnerability during pregnancy, specifically via the placenta. Yet, placental and fetal development is a temporal process with varied susceptibility to negative gestational outcomes. To addressing this gap, our laboratory utilized non-targeted proteomic analysis of amniotic fluid collected at term after either gestational day (GD) 10 or GD20 ozone exposure. Results provided a comprehensive list of proteins that indicated distinct outcome phenotypes. The acute GD20 exposure resulted in a potent acute-phase increase in antioxidant factors while the subacute GD10 exposure had a greater influence of growth factors. In follow-up, selected markers of these phenotypes will be assessed within matched placentas. Relevant to the antioxidant GD20 response, we will assess superoxide dismutase 1 (SOD1) and catalase, which catalyze superoxide and hydrogen peroxide, respectively. Per the GD10 subacute response, connective tissue growth factor (CTGF) is produced by cells involved with structure and stabilization of the ECM and affects cellular growth, migration, adhesion, and vascularization. Together with CTGF, collagen T1A2 plays a vital role in the extracellular matrix (ECM) and has been linked to pregnancy complications such as miscarriage, gestational diabetes, and preeclampsia. To assess differential impacts on the placental vasculature, we will be investigating vascular endothelial cell adhesion molecule 1 (VCAM-1) and vascular endothelial cadherin (VE-Cadherin), which have both been identified as biomarkers of preeclampsia. In our experiments, pregnant Sprague-Dawley rats were exposed once to 0.3 ppm of ozone (O3) or filtered air (FA) via whole-body inhalation at GD10 or GD20 while control animals received a sham filtered air exposure at both times. Placentas were collected and snap-frozen at GD21 followed by thin-sectioning using a frozen microtome and formaldehyde fixation. Primary antibodies to our protein targets are incubated overnight at 4C followed by secondary alexa-fluor conjugated antibodies to allow for multi-channel immunofluorescence detection. Images are generated on a Zeiss Axio Imager.M2 microscope at 200x magnification. Ongoing experiments are set at optimizing primary antibody concentrations. The experimental design involves creating three wells of two sample placental tissues per slide that are prepared and marked with primary and then secondary antibodies specific to the protein of interest. Each well contains a different dilution of the antibody that yields different fluorescence. Densitometric analysis is used to determine the concentration with the greatest signal-to-noise ratio. Once optimized, antibodies will be co-imaged on placenta tissues across five replicate animal exposure per experimental group. Quantification of mean fluorescence intensity will then be tabulated across decidual, labyrinth and chorionic placental lamina. Results will be assessed using analysis of variance with post-hoc testing for group differences. Expected outcomes will demonstrate the relationship between prior amniotic fluid proteomic findings and effects within the placenta while differentiating placental vulnerability across windows of gestation. These findings will prove significant in understanding outcomes at term for both the mother and fetus when exposed to ozone pollution.https://scholarscompass.vcu.edu/uresposters/1432/thumbnail.jp

Ecological Impacts of the 2015/16 El Niño in the Central Equatorial Pacific

The authors thank Cisco Werner (NOAA/NMFS) for proposing this special issue and encouraging our submission. We thank each of the editors, Stephanie Herring, Peter Stott, and Nikos Christidis, for helpful guidance and support throughout the submittal process. We also thank each of the anonymous external reviewers for thoughtful guidance and suggestions to improve the manuscript. REB, TO, RV, AH, and BVA are grateful for support from the NOAA Coral Reef Conservation Program. AC acknowledges support from the National Science Foundation for the following awards: OCE 1537338, OCE 1605365, and OCE 1031971. This is PMEL contribution no. 4698. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. government. The views expressed in the article are not necessarily those of the U.S. government. (NOAA Coral Reef Conservation Program; OCE 1537338 - National Science Foundation; OCE 1605365 - National Science Foundation; OCE 1031971 - National Science Foundation

Tractable Pathfinding for the Stochastic On-Time Arrival Problem

We present a new and more efficient technique for computing the route that maximizes the probability of on-time arrival in stochastic networks, also known as the path-based stochastic on-time arrival (SOTA) problem. Our primary contribution is a pathfinding algorithm that uses the solution to the policy-based SOTA problem---which is of pseudo-polynomial-time complexity in the time budget of the journey---as a search heuristic for the optimal path. In particular, we show that this heuristic can be exceptionally efficient in practice, effectively making it possible to solve the path-based SOTA problem as quickly as the policy-based SOTA problem. Our secondary contribution is the extension of policy-based preprocessing to path-based preprocessing for the SOTA problem. In the process, we also introduce Arc-Potentials, a more efficient generalization of Stochastic Arc-Flags that can be used for both policy- and path-based SOTA. After developing the pathfinding and preprocessing algorithms, we evaluate their performance on two different real-world networks. To the best of our knowledge, these techniques provide the most efficient computation strategy for the path-based SOTA problem for general probability distributions, both with and without preprocessing.Comment: Submission accepted by the International Symposium on Experimental Algorithms 2016 and published by Springer in the Lecture Notes in Computer Science series on June 1, 2016. Includes typographical corrections and modifications to pre-processing made after the initial submission to SODA'15 (July 7, 2014