Browsing by Subject "Multi-scale modeling"
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Item Development of the multi-scale dynamic modeling techniques for multi-modal brain recordings(2023-08) Chang, Yin-Jui; Santacruz, Samantha R.; Millán, José del R; Hamilton, Liberty; Castillo, EdwardFundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied in the micro- to meso-scale in studying the connections between the dynamical patterns and the behaviors, the investigation of the neural population dynamics is mainly limited by the single-scale analysis. My goal is to develop a multi-scale dynamical model for the collective activity of neuronal populations. First, I introduced a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales (Chapter 2). The NBGNet not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. I also show that the NBGNet robustly predicts held-out data across a long time scale (two weeks) without retraining. The effective connectivity defined from the presented model agrees with the established neuroanatomical hierarchy of motor control in the literature. I then introduced the m̲ulti-s̲cale n̲eural d̲ynamics n̲eural o̲rdinary d̲ifferential e̲quation (msDyNODE) to uncover multiscale brain communications governing cognitive behaviors (Chapter 3). I demonstrated that msDyNODE successfully captured multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived casual interactions between recording channels and scales not only aligned well with the abstraction of the hierarchical anatomy of the mammalian nervous system but also exhibited behavioral dependences. This work offers a new approach for in depth mechanistic studies on the brain where simultaneous acquisition of multi-scale neural activity is available. While the traditional neuronal tracing method serves as gold standard to assess the neural connectivity, toxicity and the limited efficiency prevent it from serving as a reliable tool to validate the multi-scale connectivity. It has been demonstrated that dendritic spines increase or become bigger after long-term potentiation while decrease or become smaller after long-term depression. Thus, we hypothesized the existence of excitatory and inhibitory connectivity can be revealed by the dynamics of dendritic spines. Since the dendritic spines can be smaller than the Abbe diffraction limit (~200 nm), we need an in-vivo super-resolution microscopy to achieve our goal. As a super-resolution imaging method, stimulated emission depletion (STED) microscopy has unraveled fine intracellular structures and provided insights into nanoscale organizations in cells. Although image resolution can be further enhanced by continuously increasing the STED-beam power, the resulting photodamage and phototoxicity are major issues for real-world applications of STED microscopy. In Chapter 4, I demonstrated that, with 50% less STED-beam power, the STED image resolution can be improved up to 1.45-fold using the separation of photons by a lifetime tuning (SPLIT) scheme combined with a deep learning-based phasor analysis algorithm termed flimGANE (f̲luorescence l̲ifetime i̲m̲aging based on a g̲enerative a̲dversarial n̲e̲twork). This work offers a new approach for STED imaging in situations where only a limited photon budget is available. In summary, I emphasized that this is the first time we are able to infer the nonlinear multiscale interactions in the brain networks by our multi-scale dynamic modeling approach. This opens a window of opportunity that multi-scale connectivity can provide a mechanistic understanding of brain computations underlying behaviors or mental states.Item Scale-up of reactive processes in heterogeneous media(2014-12) Singh, Harpreet, active 21st century; Srinivasan, SanjayPhysical and chemical heterogeneities cause the porous media transport parameters to vary with scale, and between these two types of heterogeneities geological heterogeneity is considered to be the most important source of scale-dependence of transport parameters. Subsurface processes associated with chemical alterations result in changing reservoir properties with interlinked spatial and temporal scale, and there is uncertainty in the evolution of those properties and the chemical processes. This dissertation provides a framework and procedures to quantify the spatiotemporal scaling characteristics of reservoir attributes and transport processes in heterogeneous media accounting for chemical alterations in the reservoir. Conventional flow scaling groups were used to assess their applicability in scaling of recovery and Mixing Zone Length (MZL) in presence of chemical reactivity and permeability heterogeneity through numerical simulations of CO₂ injection. It was found out that these scaling groups are not adequate enough to capture the scaling of recovery and transport parameters in the combined presence of chemical reactivity and physical heterogeneity. In this illustrative example, MZL was investigated as a function of spatial scale, temporal scale, multi-scale heterogeneity, and chemical reactivity; key conclusions are that 1) the scaling characteristics of MZL distinctly differ for low permeability and high permeability media, 2) heterogeneous media with spatial arrangements of both high and low permeability regions exhibit scaling characteristics of both high and low permeability media, 3) reactions affect scaling characteristics of MZL in heterogeneous media, 4) a simple rescaling can combine various MZL curves by merging them into a single MZL curve irrespective of the correlation length of heterogeneity, and 5) estimates of MZL (and consequently predictions of oil recovery) will fluctuate corresponding to displacements in a permeable medium whose lateral length is smaller than the correlation length of geological formation. We illustrate and extend the procedure of estimating Representative Elementary Volume (REV) to include temporal scale by coupling it with spatial scale. The current practice is to perform spatial averaging of attributes and account for residual variability by calibration and history matching. This results in poor predictions of future reservoir performance. The proposed semi-analytical technique to scale-up in both space and time provides guidance for selection of spatial and temporal discretizations that takes into account the uncertainties due to sub-processes. Finally, a probabilistic particle tracking (PT) approach is proposed to scale-up flow and transport of diffusion-reaction (DR) processes while addressing multi-scale and multi-physics nature of DR mechanisms and also maintaining consistent reservoir heterogeneity at different levels of scales. This multi-scale modeling uses a hierarchical approach which is based on passing the macroscopic subsurface heterogeneity down to the finer scales and then returning more accurate reactive flow response. This PT method can quantify the impact of reservoir heterogeneity and its uncertainties on statistical properties such as reaction surface area and MZL, at various scales.