Currently, my research focuses on understanding the mechanisms underlying changes (or plasticity) in the structural and electrical properties of neurons during learning and development or following trauma and rehabilitation. Under some circumstances, these changes are thought to provide an important mechanism for controlling cell function and behavior, while in others, function is sometimes maintained in spite of alterations in neuronal morphology or electrophysiology. Moreover, global features of neuronal morphology and cell dynamics appear to be under homeostatic control. In my lab, computational and mathematical approaches provide the means for integrating physiological and morphological data and supply the theoretical framework for understanding how these changes affect cell excitability and network behavior. This research in neural computation interfaces with my ongoing efforts in the area of neuroinformatics, where I am one of the developers of NeuroML, which is an extensible markup language (XML) for the computational neuroscience community that focuses on model specification and morphological data archival and exchange.
Activity-dependent Changes in Motoneurons following Spinal Cord Injury (SCI): During the chronic stage following SCI, enhanced persistent inward currents are thought to contribute to prolonged self-sustained firing in motoneurons, which is known to be one factor in muscle spasticity. In addition, there is experimental evidence for extensive neuroanatomical changes in these cells following SCI. With my collaborator Ranu Jung and other members of the Center for Adaptive Neural Systems at ASU, I have used mathematical and computational approaches to understand the roles of alterations in electrophysiology and morphology in motoneuron dynamics following SCI. Currently, we are extending these studies to the molecular and network levels. Our ultimate goal is to understand the activity-based mechanisms that underlie motoneuron restructuring following SCI in order to optimize the role of activity-based rehabilitative therapy in promoting functional recovery.
Excitability, Computation, and Activity-dependent Restructuring of Dendritic Spines: Dendritic spines are specialized compartments that are responsible for postsynaptic activity for excitatory synapses in many of the principal neurons in the brain. Experimental evidence indicates that the structure and density of dendritic spines are regulated in support of learning and memory. Using a continuum modeling approach developed by my collaborator Steve Baer of the School of Mathematical and Statistical Sciences (SoMSS) at ASU, we have modeled activity-dependent changes in dendritic spines in response to different types of synaptic activity. As part of this project, we also collaborated with Zdzislaw Jackiewicz from SoMSS in order to develop efficient computational techniques for solving these model systems. In particular, my goal is to develop a modeling framework for understanding the role of calcium and other signaling molecules in the modulation of structural plasticity in multiple biological settings.
In collaboration with Carl Gardner and Christian Ringhofer of SoMSS at ASU and Ralph Nelson at the National Institutes of Health, Dr. Baer and I have used similar techniques to create multiscale models of the subcircuits of the outer-plexiform layer of the retina. The goal of this work is to gain insight into whether electrical (ephaptic) or chemical mechanisms are responsible for the feedback observed in horizontal cells at synapses where horizontal cell spine heads contact a cone pedicle.
Structural Plasticity in Postembryonic Development: At the single neuron level, the means for interpreting spatiotemporal patterns of incoming synaptic activity is provided by the dendritic tree. The variety of neuronal morphologies is immense, yielding many potential architectures for information processing; however, which of the computations that dendrites might be able to perform are relevant to animal behavior? How do the active properties of dendritic trees shape synaptic integration and neural computation? A fundamental prerequisite for addressing these questions is the ability to relate the structure and electrophysiology of an individual neuron directly to its function. In this collaborative research effort, I am working with Carsten Duch of SoLS at ASU to investigate the function of the structural, electrophysiological, and synaptic remodeling of insect motoneurons during post-embryonic development and following genetic manipulations of potassium and calcium membrane currents. My lab has developed detailed biophysically based models of these neurons, which are constrained by electrophysiological and imaging data from the Duch laboratory. In parallel, we are using reduced mathematical models to understand the nonlinear interactions among the different channel types, membrane properties and multiple time scales and how they contribute to cell dynamics.
NeuroML for Model Specification and Neural Data Exchange: The overall goals of this collaborative, international project are to create standards that facilitate the exchange of complex neural models, allow for greater transparency and accessibility of models, enhance interoperability between simulators and other tools, and support the development of new software and databases. NeuroML is an extensible markup language that provides a common standard for neuroanatomical data, for information about the biophysical properties of membranes, and for neuronal network connectivity. Although we now have tremendous community involvement in NeuroML, my main collaborators for the project are in the laboratory of Angus Silver at University College London in the UK. The standards produced through this neuroinformatics project have been adopted by many down-stream software applications for neuroanatomical data exchange and for the specification of neuronal models.