Data assimilation for conductance-based neuronal models

This dissertation illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. Throughout this work, twin experiments, where the data is synthetically generated from output of the model, are used to validate use of these techniques for conductance-based models observing only the voltage trace. In Chapter 1, these techniques are described in detail and the estimation problem for conductance-based neuron models is derived. In Chapter 2, these techniques are applied to a minimal conductance-based model, the Morris-Lecar model. This model exhibits qualitatively different types of neuronal excitability due to changes in the underlying bifurcation structure and it is shown that the DA methods can identify parameter sets that produce the correct bifurcation structure even with initial parameter guesses that correspond to a different excitability regime. This demonstrates the ability of DA techniques to perform nonlinear state and parameter estimation, and introduces the geometric structure of inferred models as a novel qualitative measure of estimation success. Chapter 3 extends the ideas of variational data assimilation to include a control term to relax the problem further in a process that is referred to as nudging from the geoscience community. The nudged 4D-Var is applied to twin experiments from a more complex, Hodgkin-Huxley-type two-compartment model for various time-sampling strategies. This controlled 4D-Var with nonuniform time-samplings is then applied to voltage traces from current-clamp recordings of suprachiasmatic nucleus neurons in diurnal rodents to improve upon our understanding of the driving forces in circadian (~24) rhythms of electrical activity. In Chapter 4 the complementary strengths of 4D-Var and UKF are leveraged to create a two-stage algorithm that uses 4D-Var to estimate fast timescale parameters and UKF for slow timescale parameters. This coupled approach is applied to data from a conductance-based model of neuronal bursting with distinctive slow and fast time-scales present in the dynamics. In Chapter 5, the ideas of identifiability and sensitivity are introduced. The Morris-Lecar model and a subset of its parameters are shown to be identifiable through the use of numerical techniques. Chapter 6 frames the selection of stimulus waveforms to inject into neurons during patch-clamp recordings as an optimal experimental design problem. Results on the optimal stimulus waveforms for improving the identifiability of parameters for a Hodgkin-Huxley-type model are presented. Chapter 7 shows the preliminary application of data assimilation for voltage-clamp, rather than current-clamp, data and expands on voltage-clamp principles to formulate a reduced assimilation problem driven by the observed voltage. Concluding thoughts are given in Chapter 8

Daily electrical activity in the master circadian clock of a diurnal mammal

From eLife via Jisc Publications RouterHistory: collection 2021, received 2021-03-08, accepted 2021-10-09, pub-electronic 2021-11-30Publication status: PublishedFunder: Biotechnology and Biological Sciences Research Council; FundRef: http://dx.doi.org/10.13039/501100000268; Grant(s): BB/P009182/1Funder: Biotechnology and Biological Sciences Research Council; FundRef: http://dx.doi.org/10.13039/501100000268; Grant(s): BB/S01764X/1Funder: Biotechnology and Biological Sciences Research Council; FundRef: http://dx.doi.org/10.13039/501100000268; Grant(s): BB/N014901/1Funder: Wellcome Trust; FundRef: http://dx.doi.org/10.13039/100004440; Grant(s): 210684/Z/18/ZFunder: National Science Foundation; FundRef: http://dx.doi.org/10.13039/100000001; Grant(s): DMS 155237Funder: Army Research Office; FundRef: http://dx.doi.org/10.13039/100000183; Grant(s): W911NF-16-1-0584Funder: US-UK Fulbright Commission; FundRef: http://dx.doi.org/10.13039/501100000592Funder: Engineering and Physical Sciences Research Council; FundRef: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/N014391/1Circadian rhythms in mammals are orchestrated by a central clock within the suprachiasmatic nuclei (SCN). Our understanding of the electrophysiological basis of SCN activity comes overwhelmingly from a small number of nocturnal rodent species, and the extent to which these are retained in day-active animals remains unclear. Here, we recorded the spontaneous and evoked electrical activity of single SCN neurons in the diurnal rodent Rhabdomys pumilio, and developed cutting-edge data assimilation and mathematical modeling approaches to uncover the underlying ionic mechanisms. As in nocturnal rodents, R. pumilio SCN neurons were more excited during daytime hours. By contrast, the evoked activity of R. pumilio neurons included a prominent suppressive response that is not present in the SCN of nocturnal rodents. Our modeling revealed and subsequent experiments confirmed transient subthreshold A-type potassium channels as the primary determinant of this response, and suggest a key role for this ionic mechanism in optimizing SCN function to accommodate R. pumilio’s diurnal niche

Virchow: A Million-Slide Digital Pathology Foundation Model

Computational pathology uses artificial intelligence to enable precision medicine and decision support systems through the analysis of whole slide images. It has the potential to revolutionize the diagnosis and treatment of cancer. However, a major challenge to this objective is that for many specific computational pathology tasks the amount of data is inadequate for development. To address this challenge, we created Virchow, a 632 million parameter deep neural network foundation model for computational pathology. Using self-supervised learning, Virchow is trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue groups, which is orders of magnitude more data than previous works. When evaluated on downstream tasks including tile-level pan-cancer detection and subtyping and slide-level biomarker prediction, Virchow outperforms state-of-the-art systems both on internal datasets drawn from the same population as the pretraining data as well as external public datasets. Virchow achieves 93% balanced accuracy for pancancer tile classification, and AUCs of 0.983 for colon microsatellite instability status prediction and 0.967 for breast CDH1 status prediction. The gains in performance highlight the importance of pretraining on massive pathology image datasets, suggesting pretraining on even larger datasets could continue improving performance for many high-impact applications where limited amounts of training data are available, such as drug outcome prediction

Planning to execution earned value risk management tool

This study presents a proof-of-concept software application capable of producing risk-based estimates for project costs and schedules, tracking project performance against baseline plans, and providing real-time updates for cost and schedule at completion. Current budget and schedule estimation practices within the Department of Defense (DOD) are often performed by different personnel across several software tools. Market research of existing commercial off-the-shelf (COTS) project planning and tracking software shows that tools commonly used by many industries require additional software packages and user training to perform uncertainty analyses. A modeling and simulation experiment demonstrates how using minimum and maximum duration estimates can result in baseline project plans that greatly underestimate and overestimate the project schedule, respectively. The prototype software application integrates the planning and execution tracking of projects. It accounts for uncertainty within the various activities in a project, thus creating more realistic baseline plans. During project execution, the prototype software application shortens the project status feedback cycle, identifies any activities that go over cost or schedule, and guides management action by determining which activities have the greatest impact on the outcome of the project. It is easier to use when compared to current planning and execution tracking software applications.http://archive.org/details/planningtoexecut1094547326Approved for public release; distribution is unlimited

Daily electrical activity in the master circadian clock of a diurnal mammal

This is the final version. Available on open access from eLife Sciences Publications via the DOI in this recordData availability: All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 2, 3 and 6. Code for simulating our conductance-based models is available in ModelDB (McDougal et al 2017, J Comput Neurosci) at http://modeldb.yale.edu/267183. Code for performing neuronal data assimilation (neuroDA) to infer model parameters from current-clamp recordings is available at https://github.com/mattmoye/neuroDA; copy archived at https://archive.softwareheritage.org/swh:1:rev:faf27e9035c28320feb2f82c37bd2bb8e0fc0fbd.Circadian rhythms in mammals are orchestrated by a central clock within the suprachiasmatic nuclei (SCN). Our understanding of the electrophysiological basis of SCN activity comes overwhelmingly from a small number of nocturnal rodent species, and the extent to which these are retained in day-active animals remains unclear. Here, we recorded the spontaneous and evoked electrical activity of single SCN neurons in the diurnal rodent Rhabdomys pumilio, and developed cutting-edge data assimilation and mathematical modeling approaches to uncover the underlying ionic mechanisms. As in nocturnal rodents, R. pumilio SCN neurons were more excited during daytime hours. By contrast, the evoked activity of R. pumilio neurons included a prominent suppressive response that is not present in the SCN of nocturnal rodents. Our modeling revealed and subsequent experiments confirmed transient subthreshold A-type potassium channels as the primary determinant of this response, and suggest a key role for this ionic mechanism in optimizing SCN function to accommodate R. pumilio's diurnal niche.Biotechnology and Biological Sciences Research Council (BBSRC)Wellcome TrustNational Science Foundation (NSF)Army Research OfficeUS-UK Fulbright CommissionEngineering and Physical Sciences Research Council (EPSRC

The E3 ubiquitin ligase adaptor Tango10 links the core circadian clock to neuropeptide and behavioral rhythms

Circadian transcriptional timekeepers in pacemaker neurons drive profound daily rhythms in sleep and wake. Here we reveal a molecular pathway that links core transcriptional oscillators to neuronal and behavioral rhythms. Using two independent genetic screens, we identified mutants of Transport and Golgi organization 10 (Tango10) with poor behavioral rhythmicity. Tango10 expression in pacemaker neurons expressing the neuropeptide PIGMENT-DISPERSING FACTOR (PDF) is required for robust rhythms. Loss of Tango10 results in elevated PDF accumulation in nerve terminals even in mutants lacking a functional core clock. TANGO10 protein itself is rhythmically expressed in PDF terminals. Mass spectrometry of TANGO10 complexes reveals interactions with the E3 ubiquitin ligase CULLIN 3 (CUL3). CUL3 depletion phenocopies Tango10 mutant effects on PDF even in the absence of the core clock gene timeless. Patch clamp electrophysiology in Tango10 mutant neurons demonstrates elevated spontaneous firing potentially due to reduced voltage-gated Shaker-like potassium currents. We propose that Tango10/Cul3 transduces molecular oscillations from the core clock to neuropeptide release important for behavioral rhythms