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MBI Postdoctorate Seminar Speaker: Dr. John J. Enyeart,
Department of Neuroscience, OSU
MBI Seminar Speaker: Roman Borisyuk,
University of Plymouth Oscillatory neural model (ONM) with traditional Hebbian-like learning rule for associative memorization of sequences is considered. ONM of novelty detection which is based on frequency encoding of input information and oscillatory mechanism of memory formation will be presented. The adaptation of natural frequencies of network oscillators to the frequency of the input signal is used as the mechanism of information memorization. The recognition principle for familiar stimuli is based on the resonance amplification of network activity. A new ONM that combines consecutive selection of objects, attention focus formation, memorisation, and discrimination between new and familiar objects has been developed. The model works with visual information and fulfils the following operations: (1) separation of different objects according to their spatial connectivity; (2) consecutive selection of objects located in the visual field into the attention focus; (3) representation of objects in the working memory; and (4) novelty detection of objects.
MBI Seminar Speaker: Gustavo Leone, James
Cancer Hospital, OSU Presentation Materials: PPT
MBI Postdoctorate Seminar Taste buds are collections of 50 to 100 individual taste receptor cells (TRCs). These cells detect the presence of chemical stimuli within the oral cavity and relay this information to the central nervous system via synaptic connections with afferent nerve fibers. The "historical" view of this process describes a single TRC as becoming depolarized when stimulated by tastants by elusive transduction mechanisms and subsequently releasing transmitter onto the afferent nerve. Many TRCs were known not to synapse with the afferent nerve fibers and were believed to be supporting cells. Recent developments in the physiology and molecular biology of TRCs have made it clear that this view is no longer tenable. Classes of recently cloned taste receptor molecules for bitter and sweet stimuli and related transduction enzymes have been localized to TRCs that lack synapses to the afferent nerve. How then do these cells, which possess the machinery to respond to taste stimuli, relay this information to the central nervous system? Our laboratory has discovered several signaling pathways endogenous to the taste bud that can serve as mechanisms for cell to cell communication among the TRCs of the bud. These include classic neurotransmitters, such as norepinephrine and serotonin, and neuropeptides, such as cholecystokinin. For the most part, it appears that certain subsets of TRCs express a signaling agent while other subsets of TRCs express a receptor for this agent. As well, physiological responses can be measured to exogenous application of these agents. Thus these pathways may serve unrecognized manners of information processing and modulation of the gustatory signal prior to afferent nerve stimulation.
MBI Seminar Speaker: Monica K. Hurdal,
Department of Mathematics, Florida State University The cortical surface of the human brain is a highly folded, convoluted structure that is 3-5 mm thick and topologically equivalent to a sheet. Most of the functional processing of the brain occurs on this sheet. However, the folding patterns vary considerably between individuals in terms of the shape, depth, length and location of the folds. As a result, neuroscientists are interested in 2D analysis methods and tools for comparing function and anatomy across subjects which can take into account some of this individual variability. I am using methods from complex analysis, computational geometry, topology and image processing to "unfold" and "flatten" the cortical surface of the brain. While it is impossible to create areal or length preserving maps of a highly convoluted surface such as the brain, the Riemann Mapping Theorem describes the existence of conformal maps. I will discuss methods and theory from the area of "circle packings" which I am using to create approximations to discrete conformal maps of the brain. I will discuss some of the computational and topological issues that arise and how I am imposing coordinate systems on these maps. I will show some of the brain maps that I have created and discuss collaboration results with neuroscientists who are interested in depression, schizophrenia and Alzheimer's diseases.
MBI Seminar Speaker: Dr. Roman Borisyuk,
University of Plymouth Presentation Materials: PPT Oscillatory Neural Networks (ONN) will be described and compared with other NN paradigms. ONN models of attention and novelty detection will be presented. These models are based on principle of synchronization of neural activity which is a very powerful principle of computational neuroscience.
MBI Postdoctorate Seminar Speaker: Dr. Andrej Rotter,
Department of Pharmacology, The Ohio State University The cerebellum is a region of the central nervous system responsible for the coordination of complex motor movements. It consists of bilaterally symmetrical nuclei surrounded by a highly convoluted cortex. Numerous anatomical, physiological and biochemical studies have resulted in a detailed description of its cellular and molecular components, circuitry and sequence of developmental events. Nevertheless, despite the wealth of descriptive data, our understanding of the rules and relationships leading to the formation of the cerebellum and to the acquisition of its functional properties is lagging behind. In this presentation the current state of our knowledge concerning the cerebellum will be outlined and potential opportunities for mathematical modeling will be suggested.
MBI Postdoctorate Seminar Speaker: Dr. Sharon Crook,
University of Maine
MBI Postdoctorate Seminar In this (informal) talk we will present a brief overview of cluster analysis, and discuss its application to the diagnosis of breast cancer using gene expression (micro) arrays. The talk will present some common clustering algorithms, and describe several statistical problems that arise in the application and interpretation of clustering methods
Weekly Postdoctorate Seminar/Discussion Group:
Speaker: Dr. Michael Beattie, Department of Neuroscience This seminar is based on converging evidence from two very different experimental systems that involve glutamate as a neurotransmitter, and as an excitotoxic agent that kills neurons after injury. Much of the excitatory synaptic transmission in the CNS is mediated by glutamate through post-synaptic receptors that open ion channels in response to glutamate release. Recent evidence suggests that changes in excitability at these synapses such as those that occur in long term depression (LTD) or potentiation (LTP) are due, in part, to rapid changes in the number of receptors located at the synapse due to modulation of receptor recycling. Extracellular glutamate increases rapidly after injury to the CNS, and very high levels can cause neuronal and glial death by increasing intracellular Ca++ to toxic levels. Tumor necrosis factor-alpha (TNF) is an inflammatory cytokine that is released by immune cells and can induce necrotic and apoptotic death in target cells. After injury to the CNS, TNF levels increase markedly, and TNF has been thought to be a possible mediator of secondary cell death after stroke, spinal cord injury (SCI), and also in multiple sclerosis (MS). TNF also seems to increase glutamate actions in a brainstem circuit mediating gastric motility (Emch, Hermann, and Rogers, 2000). We tested the hypothesis that TNF and glutamate might interact after SCI by making nano-injections of TNF or the glutamate agonist kainic acid (KA) into the spinal cord gray matter either alone or together (Hermann et al, 2001). Low doses or either agent produced no cell death; together, they killed large numbers of neurons within 90 minutes. This potentiation of cell death was completely blocked by CNQX, which is an antagonist at AMPA-type glutamate receptors. This suggested that TNF might in some way be altering AMPA-R excitability. To test this, we engaged the help of collaborators at Stanford and applied TNF to hippocampal neurons in culture (E. Beattie et al, 2002). 15 minutes after application of TNF, AMPARs on the surface of dendrites increased dramatically. Whole cell patch clamp showed a concomitant increase in spontaneous excitatory post-synaptic currents. Reduction of endogenous TNF by antibody treatment reduced AMPAR surface expression and epsc's. Experiments in hippocampal slice also showed a role for TNF in modulating synaptic activity. We showed that changes in extracellular K+ changed both AMPAR surface expression and susceptibility of cerebellar granule cells to excitotoxic cell death (Ha et al, 2002). Thus it appears that modulation of AMPARs by TNF (and other agents) may be involved in both synaptic plasticity (and learning?) and the induction of cell death. There are some therapeutic implications of these findings.
Speaker: Dr. Karl Obrietan, Department of Neuroscience Circadian rhythms of behavior and physiology are observed in a
variety of organisms. In mammals, the suprachiasmatic nuclei (SCN)
of the hypothalamus function as the major biological clock. The
inherent pacemaker activity of the SCN can be effectively regulated
by changes in the environmental light cycle. This allows an animal
to synchronize its internal clock with the ever-changing light cycle
encountered over a seasonal/yearly basis. What are the cellular
events that allow light to affect clock timing? Recent work has
revealed that coordinated transcriptional oscillations are required
for circadian rhythm generation and that light-activated signaling
events entrain the clock by resetting transcriptional rhythms. Given
these observations, a characterization of the intracellular signaling
pathways and downstream transcription factors activated by light
will be critical for understanding the functional properties of
the circadian clock. I will present data examining the role of the
p42/p44 mitogen-activated protein kinase (MAPK) signal transduction
pathway as a cellular signaling intermediate that couples light
to circadian clock entrainment. If there is time left over, I will
also present data looking at mechanisms of Ca2+-induced gene expression
in
Speaker: Gheorghe Craciun - Post-Doctorate Seminar/Discussion
Group We describe the general setting for chemical reaction network theory
based on mass-action kinetics. Recent methods are able to draw conclusions
about the dynamics of the chemical composition, even in the absence
of specific information about the reaction rate parameters. We present
some of these methods and connections to cell and molecular biology.
Speaker: Chris Fall Speaker: Raghu Raghavan,
References Physical modeling in biomedical computing: a research agenda A continuum-mechanical model for cortical growth Form from growth: a mechanical model for cortical development and
its control Corticography, Jacobi fields, and symmetries.
We develop an algorithm for analysing perturbation-induced stability-instability properties of systems and such a technique may be used to actuate a noise-induced activation in various biological/clinical systems. Stochastic enhancement in neural, immunological, chemical and radiological processes is shown. We describe the mathematical analysis and experimental findings of such activation in biological/neural processes and elucidate applications towards diagnosis and therapy, in neurology, radiology and oncology, with special applications for management to brain lesions. The associated phenomenon of non-equilibrial de-stabilization of a pathological system under artificially engineered perturbation, is analysed in terms of Prigogine-Glansdorff Stability Theorem and we explore a computational model of fluctuational transitions in complex systems in general. Collins, J, Gregg, P. (1997). Noise-mediated enhancements and decrements in human sensation, Phys Rev E, 56, 923-26. Glass, L. (2002). Synchronization and rhythmic processes in physiology, Nature, 410, 277-84. Horsthemke, W., Lefever, R (1994). Noise induced transitions in physics and biology, Springer, Berlin-N.Y. Hahn, H, et al (1974). Threshold excitations and effect of noise in an enzyme system, PNAS, 71, 4067-71. Miller, J & Levin, R,. Stochastic resonance in neurone, Nature 380, 165-168, 1996. Paulsson, J, Berg, O, Ehrenberg, M. (2000). Stochastic Focussing: Fluctuation-enhanced sensitivity in cellular regulation, PNAS. 97(13), 7148-53,. Roy, P, Kozma, R, et al (2002). >From Neurocomputation to Immunocomputation: A method and algorithm for fluctuation-induced instability in biological systems, IEEE Trans. Evolutionary Computing, 6(3), 1-14, 2002. Roy, P. et al (2000). Tumour Stability Analysis, Kybernetes: Intl J of Systems Science, 29, 896-926. Simonotto, E, et al, (1997). Visual perception of stochastic resonance, Phys Rev Lett 78:1186-88. Deep brain stimulation (DBS) represents a dramatically effective treatment for clinically intractable movement disorders such as essential tremor and Parkinson’s disease; however the underlying mechanisms of its therapeutic action are unknown. The goal of my research program is to develop a systems level understanding of the effects DBS using detailed computer modeling techniques. My work couples the results of functional imaging and basic neurophysiology to computer models of extracellular electric fields and their effects on the nervous system. These models consist of three basic stages. The first step is the development of 3D finite element models of the electric field generated by DBS electrodes where the electrical properties of the tissue are based on diffusion tensor MRI. The second step is coupling the electric field to 3D reconstructions of neurons surrounding the electrode where the ion channel biophysics of the neuron models are based on experimental data. The outcome of steps 1 & 2 are predictions on the volume of tissue surrounding the electrode that are affected by the stimulation. The final step is then to apply those stimulation effects to large scale neuronal network models of the thalamo-cortical-basal ganglia system that DBS modulates thereby providing experimentally testable hypotheses on the effects of stimulation in the different nuclei of the network. In addition, the results of this work can be coupled to PET/fMRI to provide a contiunum from the single cell to the network to the behavior.
Speaker:Sanjay
Danthi Adrenal Zona Fasciculata (AZF) cells in the adrenal cortex secrete cortisol in response to ACTH, a hormone released by the pituitary gland. ACTH acts by depolarizing these cells leading to Ca+2 influx through T-type Ca+2 channels, which subsequently causes Ca+2 dependent cortisol release. Our laboratory has discovered an ATP activated, non-inactivating background K+ channel (Iac/TREK-1/KCNK2) in bovine AZF cells that serves to set the resting membrane potential of the cell (Mlinar et al, 1993; Enyeart et al, 1997). ACTH inhibits this channel at physiological concentrations leading to membrane depolarization (Mlinar et al, 1993). Thus our laboratory has identified the link between ACTH and cortisol secretion. In my talk I will discuss some of the important properties of this channel such as nucleotide dependence, agonists and antagonists and its cloning (Xu and Enyeart, 2001; Enyeart et al, 2002.). I will also briefly talk about some of the electrophysiological techniques that I have used in my work such as whole cell and single channel patch clamp recordings. Finally I would mention our idea of modeling the ionic currents in the bovine AZF cells to generate a model that would identify possible membrane potential oscillations and help determine the voltage dependent entry of Ca+2 into the cell necessary for optimal release of cortisol. References: "I describe a model of feedback from the primary visual cortex to the lateral geniculate nucleus, and use it to study the sensitivity to orientation discontinuity of lateral geniculate relay cells. I compare with experimental results and make a number of testable predictions."
The inputs that nervous systems receive, and the outputs they produce, vary over a wide range of time scales. For instance, music commonly requires producing (for the musician) and interpreting (for the listener) tone intervals that vary at least a sixteen-fold (16th vs. whole notes). Membrane conductance time constants vary over a thousand-fold range (msec to sec). This wide temporal range suggests that individual neurons could have complex inherent responses to varying temporal input. Experiment and modeling we have performed in the lobster pyloric neuromuscular system support this hypothesis. First, the pyloric network produces a phase constant output over a five-fold cycle period range, and this property depends in part on individual network neurons being able to 'measure' input cycle period. These neurons provide mechanisms both for producing the 'same' motor pattern over a wide period range (fast vs. slow walking) and for identifying all rhythmic on-off patterns (e.g., music beat lines). With slight modification, models of these neurons can also reproduce the known duration sensitive neurons (which, among other things, allow us to analyze music melody). Second, pyloric muscle relaxation time constants are much longer than pyloric cycle period. The muscles consequently demodulate slow modulations in pyloric network activity imposed by other networks, and therefore largely to exclusively contract in time with these other, non-innervating, networks. Different muscles can extract different frequency bands, and hence contract with different modulating networks, even when driven by identical input. Different intracellular second messenger systems have different time constants, and thus should similarly selectively respond to different frequency bands in broad band input. These observations suggest that, although network-based mechanisms are undoubtedly important, individual neurons can perform relatively complicated analyses of patterned temporal input. The presence of such neurons in neural networks complicates the effects and analysis of network response to entraining input (i.e., when is simple phase oscillator coupling theory appropriate), and I will also present our beginning work on this subject.
Stochastic Resonance (SR) is a phenomenon observed in nonlinear systems whereby the introduction of noise enhances the detection of a subthreshold signal for a certain range of noise intensity. SR has been observed in many physical and mathematical systems. The nonlinear threshold detection mechanism that neurons employ and the noisy environment in which they reside make it likely that SR plays a role in neural signal detection. While the role of SR in sensory neural systems has been studied, its function in central neurons is unknown. In many central neurons, such as the hippocampal CA1 cell, very large dendritic trees are responsible for detecting neural input in a noisy environment. Attenuation due to the electrotonic length of these trees is significant, suggesting that a method other than passive summation is necessary if signals at the distal ends of the tree are to be detected. The hypothesis that SR is involved in the detection of distal synaptic inputs was first tested in a computer simulation of a CA1 cell and then verified with in vitro rat hippocampal slices. The results strongly showed that SR can enhance signal detection in CA1 hippocampal cells. High levels of noise were found to equalize detection of synaptic signals received at varying positions on the dendritic tree. The amount of noise needed to evoke the effect is comparable with physiological noise in slices and in vivo. Computer simulations show that the phenomenon is enhanced in neuronal networks. Therefore both computer simulations and experiments suggest an important role for stochastic resonance in neuronal signal processing. Speaker: Geraldine
Wright The olfactory system must be able represent a highly variable stimulus in such a way that an animal may recognize similar stimuli and discriminate among dissimilar ones. How an animal generalizes from its experience of an odor mixture to a new experience with another odor mixture may depend on the perceptual similarity of the mixtures. I will discuss behavioral and physiological evidence for odor coding in the olfactory system of insects. I will also discuss my research plans for my time as a postdoc at the MBI. Relaxation oscillations (RO), a highly nonlinear type of oscillation,
are found in many biological, chemical, physical and neuronal problems.
The characteristic feature of RO is a repeated switching between
fast and slow motions. We will study the well known forced van der
Pol oscillator, a model for a triode circuit. This oscillator exhibits
all kinds of dynamical behaviour from synchronization up to 'chaos'.
We will explain some of these properties by using techniques from
dynamical systems, especially geometric singular perturbation theory
(GSPT). We develop minimal computational models to investigate how intrinsic cellular mechanisms can modify responses to dynamic sounds. Our models are based on experimental data, recorded from phase-disparity-sensitive neurons in auditory midbrain. I will talk about the experimental background of this work, describe our models, and show the results and their connection with experimental work. Reaction-diffusion systems, complex biochemical reaction chains, population dynamics, and many more natural phenomena are stochastic processes in which many different events can occur at any time. The simulation of such systems is a formidable task, and I will present some algorithms that allow for the efficient simulation of large systems of stochastic processes. Briefly, the idea is to use Gillespie's algorithm to determine the time interval between any two events, and then use logarithmic classification of possible events to determine efficiently which event occurs at any one time step. Utilizing proper data structures, this classification and selection scheme can be implemented in a highly time-efficient manner (Fricke and Wendt, 1995). I will give an introduction into the ideas underlying the logarithmic
classification algorithm and give a brief tutorial on how to use
the logarithmic class library in simulation code. Speaker: Katarzyna
Rejniak Results of clinical research show strong connections between appearance
of deep invaginations of the trophoblast tissue and pregnancy complications.
Because the trophoblast tissue grows only by cell proliferation
and fusion, it is important to analyse how the initiation of these
processes can cause the tissue to bend. In my talk, I will show
how the trophoblast tissue can be modeled using the immersed boundary
method and will present simulations of tissue development, along
with comparisons with clinically obtained results. Speaker: Peter
Jung Ion channels and receptors in the cell membranes and internal membranes
are often distributed in discrete clusters. One particularly well
studied example is the distribution of inositol 1,4,5-triphosphate
receptors in the plasma membrane that controls the flux of Ca2+
from the endoplasmic reticulum into the cytosol. By using mathematical
modeling, we show that channel clustering can enhance the cell's
Ca2+ signaling capability.
Speaker: Daniel
P. Dougherty Microbial fermentations, no matter whether they take place in "defined'' growth media or out in nature, occur in complex dynamic environments. The purpose of this talk will be to discuss a predictive modeling approach which handles this complexity while maintaining a mathematically tractable modeling framework. During this talk, the semi-mechanistic approach will be introduced by a few examples. Some basic fermentation microbiology will be provided and the approach will be applied to the study of acid tolerance in lactic acid bacteria growing in a complex medium. The work presented here may have application to other areas of research where dynamic alterations in chemical buffering are important.
Speaker: Hans
Plesser I will present some of the methods I developed during my PhD-work for the study of stochastic resonance in leaky integrate-and-fire neurons. Specifically, I will present Markov-chain based methods for the calculation of the power spectral density of spike trains evoked by sinusoidal stimuli. Based on these methods, I will investigate the influence of signal frequency and background noise on the signal-to-noise ratio of the neuronal response, and discuss some scaling properties. If anyone would like a preview, some material is available on my website http://arken.nlh.no/~itfhep/publications.html especially items 14, 7, and 8, or my opus magnum, nr 13, if you want it all in color and detail. If anyone had an idea on how to prove that the interspike-interval density of the leaky integrate-and-fire neuron driven by sinusoidal input and Gaussian white noise is strictly positive for t>0 (except possibly at isolated points), I'd be very happy about suggestions. The proof in Section 2.2.3 of my thesis is, unfortunately, badly flawed.
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