Over the last year my main interest has been the study of synaptic potentiation from an animated, quantitative perspective (read: MCMC methods and simulation). Currently, I'm examining the membrane diffusion of neurotransmitter receptors and modeling how these particles swarm and potentiate synapses. It has been an interesting transition into these topics - prior to these projects I worked primarily with brain tissue and mice, but now I find myself spending most of my day programming, running simulations, and working with equations. I'm not sure why, but I find diffusion quite interesting. Stochastic diffusion, like that in Brownian motion, is a pure actuation of the basic properties of statistics - probability distributions in particular. Given that synaptic potentiation is directly mediated by stochastic diffusion and synaptic capture of receptors, it seem that neurons have evolved into innate statistical computers. The result of 100 billion of these statistical computers making 100 trillion connections is the human brain.
It is now generally accepted that many forms of adaptive behavior, including learning and memory, engender lasting physiological changes in the brain; reciprocally, neural plasticity among the brain’s synaptic connections provides the capacity for learning and memory. Whenever I have to summarize my primary research focus using just a few words, they always include: "synaptic plasticity". Indeed, I feel that the key to fully understanding cognitive processes like memory formation is through studying neural dynamics at the cellular-network, synaptic, and molecular levels.
The study of actin dynamics is centrally important to understanding synaptic plasticity. Fortunately, actin research has provided a vast pool of experimental studies, and several quantitative models that provide excellent characterizations of actin polymerization kinetics. To simulate filament scaffolding in a dendritic model, I developed a stochastic 3D model of actin dynamics based on parameters from previously established in steady-state (Bindschadler 2004, Yarmola 2008), monte carlo (Halavatyi 2008) and stochastic (Mogilner 2006) models. The ability to simulate the evolution of actin networks in 3D makes this model unique.
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This project aims to provide annotated sets of molecular pathways involved in neural plasticity underlying learning and memory systems. In general, biological pathways display the series of interactions among molecules resulting in functional changes within cells and neural networks. Currently there are large scale projects dedicated to amassing pathway evidence via high-throughput methods. The goal is to translate this unwieldy biopathway data from several empirical databases into visually digestible material, by characterizing the features of molecular cascades most sensitive to an event of interest (e.g. fear conditioning or amphetamine addiction).
A connectome is a comprehensive map of the neural networks within the brain. It details the efferent and afferent pathways within and between brain regions. Functional Connectivity refers to the function of a particular brain region and its information processing role within a distributed neural network. The goal of this project is to create a platform where users can jump into the connectome at any given brain region and visually navigate to upstream and downstream regions; along the way, users can learn about the functional role of each brain region. All information has been collected from empirical sources and scientific databases, in particular, the Allan Brain Atlas.
Welcome to the official wiki of Brad Monk
Hello and welcome to my wiki. This is where I stash random information and have every intention of linking it all together someday. If you are so inclined, recent additions to this wiki can be found in the box on the right. For a non-curated glimpse of my activity you can check out the latest wiki updates. Older wiki content can be accessed using the [search box] or perusing all pages. If you would like to contact me, you can find this info on my home page.