Metasimulation and Workflow Automation with Application in Computational Neuroscienc

Increasing model complexity, higher numbers of simulations, and improved computational resources make manual methods a bottleneck in computational neuroscience research. We present four progressive essays that describe new mental constructs and software tools that automate daily simulation workflow; facilitate access to servers, clusters, and cloud; automate access to a public experimental database; and form an "investigation database" which links input parameter vectors, experimental results, and post-simulation analyses.;In Essay I, "NeuroManager: A workflow analysis based simulation management engine for computational neuroscience", we developed NeuroManager, an object-oriented simulation management software engine for computational neuroscience. NeuroManager automates the workflow of simulation job submissions when using heterogeneous computational resources, simulators, and simulation tasks. The object-oriented approach 1) provides flexibility to adapt to a variety of neuroscience simulators, 2) simplifies the use of heterogeneous computational resources, from desktops to super computer clusters, and 3) improves tracking of simulator/simulation evolution. We implemented NeuroManager in MATLAB, a widely used engineering and scientific language, for its signal and image processing tools, prevalence in electrophysiology analysis, and increasing use in college Biology education. To design and develop NeuroManager we analyzed the workflow of simulation submission for a variety of simulators, operating systems, and computational resources, including the handling of input parameters, data, models, results, and analyses. This resulted in twenty-two stages of simulation submission workflow. The software incorporates progress notification, automatic organization, labeling, and time-stamping of data and results, and integrated access to Matlab's analysis and visualization tools. NeuroManager provides users with the tools to automate daily tasks, and assists principal investigators in tracking and recreating the evolution of research projects performed by multiple people. Overall, NeuroManager provides the infrastructure needed to improve workflow, manage multiple simultaneous simulations, and maintain provenance of the potentially large amounts of data produced during the course of a research project.;Essay II, "Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model", describes a collaborative effort in which Dr. Wondimu Teka used the NeuroManager software we had developed to perform many complex, multi-day computer simulations on remote clusters for studying fractional conductances in a neuron model. We collaborated daily with Dr. Teka to improve NeuroManager's performance, usability, clarity, transparency, and efficiency. As a result, NeuroManager was advanced and tested in an intense computational neuroscience environment.;Essay II Abstract: We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model. To implement slow-adapting power-law dynamics of the gating variables of the potassium, n, and sodium, m and h, conductances we used fractional derivatives of order eta ≤ 1. The fractional derivatives were used to solve the kinetic equations of each gate. We systematically classified the properties of each gate as a function of eta. We then tested if the full model could generate action potentials with the different power-law behaving gates. Finally, we studied the patterns of action potential that emerged in each case. Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of eta. In comparison with the classical model, the action potential shapes for power-law behaving potassium conductance (n gate) showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance (m gate), the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance (h gate) the spikes had wider peak that for low values of h replicated pituitary- and cardiac-type action potentials. With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and eta, such as square wave bursting, mixed mode oscillations, and pseudo-plateau potentials. Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.;Essay III is entitled, "Automating NEURON simulation deployment in cloud resources". Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. (Abstract shortened by ProQuest.).