A data-informed mean-field approach to mapping of cortical parameter landscapes

Neurons in the brain form thousands of connections, or synapses, with one another, allowing signals to pass from one cell to the next. To activate a neuron, a high enough activating signal or ‘action potential’ must be reached. However, the accepted view of signal transmission assumes that the great majority of synapses are too weak to activate neurons. This means that often simultaneous inputs from many neurons are required to trigger a single neuron’s activation. However, such coordination is likely unreliable as neurons can react differently to the same stimulus depending on the circumstances. An alternative way of transmitting signals has been reported in turtle brains, where impulses from a single neuron can trigger activity across a network of connections. Furthermore, these responses are reliably repeatable, activating the same neurons in the same order. Riquelme et al. set out to understand the mechanism that underlies this type of neuron activation using a mathematical model based on data from the turtle brain. These data showed that the neural network in the turtle’s brain had many weak synapses but also a few, rare, strong synapses. Simulating this neural network showed that those rare, strong synapses promote the signal’s reliability by providing a consistent route for the signal to travel through the network. The numerous weak synapses, on the other hand, have a regulatory role in providing flexibility to how the activation spreads. This combination of strong and weak connections produces a system that can reliably promote or stop the signal flow depending on the context. Riquelme et al.’s work describes a potential mechanism for how signals might travel reliably through neural networks in the brain, based on data from turtles. Experimental work will need to address whether strong connections play a similar role in other animal species, including humans. In the future, these results may be used as the basis to design new systems for artificial intelligence, building on the success of neural networks.

Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution

With growing concern about process variation in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for design and verification. However, the high dimensionality of parameter space is a serious modeling challenge for emerging VLSI technologies, where the models are increasingly more complex. In this paper, we propose and validate a feature selection method to reduce the circuit modeling complexity associated with high parameter dimensionality. Despite the commonly used methods such as <i>Principal Component Analysis</i> (PCA) and <i>Independent Component Analysis</i> (ICA), this method is capable of dealing with mixed Gaussian and non-Gaussian parameters, and performs a parameter selection in the input space rather than creating a new space. By considering non-linear dependencies among input parameters and outputs, the method results in an effective parameter selection. The application of this method is demonstrated in digital circuit timing analysis to effectively reduce the number of simulations. The experimental results on <i>Double-Gate Silicon NanoWire FET</i> (DG-SiNWFET) technology indicate 2.5× speed up in timing variation analysis of the ISCAS89-s27 benchmark with a controlled average error bound of 9.4%.

The emergence of hyperactivity in the form of elevated spontaneous firing rates after cochlear trauma has been well documented in a number of central auditory structures, including the auditory cortex, inferior colliculus, and dorsal subdivision of the cochlear nucleus. This hyperactivity is of interest as a possible neural substrate of tinnitus. Whether the ventral subdivision of the cochlear nucleus shows hyperactivity has never been investigated despite the fact that, like the dorsal division, it also receives direct input from the damaged cochlea and supplies major ascending inputs to brainstem and midbrain auditory centers. We investigated spontaneous neuronal firing rates in the ventral cochlear nucleus in a guinea pig model of cochlear trauma in which we have shown that hyperactivity consistently develops in the inferior colliculus (Mulders and Robertson, 2009). The mean spontaneous firing rates of ventral cochlear nucleus neurons was significantly elevated compared to sham controls. This hyperactivity was more evident in primary-like and onset categories of neurons. Hyperactivity in the ventral subdivision of cochlear nucleus therefore needs to be considered in relation to neural models of the genesis of tinnitus.

Chemosensitive (CS) neurons are found in discrete brainstem regions, but whether the CS response of these neurons is due to intrinsic chemosensitivity of individual neurons or is mediated by changes in chemical and/or electrical synaptic input is largely unknown. We studied the effect of synaptic blockade (11.4 mM Mg2+/0.2mM Ca2+) solution (SNB) and a gap junction uncoupling agent carbenoxolone (CAR--100 microM) on the response of neurons from two CS brainstem regions, the NTS and the LC. In NTS neurons, SNB decreased spontaneous firing rate (FR). We calculated the magnitude of the FR response to hypercapnic acidosis (HA; 15% CO2) using the Chemosensitivity Index (CI). The percentage of NTS neurons activated and CI were the same in the absence and presence of SNB. Blocking gap junctions with CAR did not significantly alter spontaneous FR. CAR did not alter the CI in NTS neurons and resulted in a small decrease in the percentage of activated neurons, which was most evident in NTS neurons from rats younger than postnatal day 10. In LC neurons, SNB resulted in an increase in spontaneous FR. As with NTS neurons, SNB did not alter the percentage of activated neurons or the CI in LC neurons. CAR resulted in a small increase in spontaneous FR in LC neurons. In contrast, CAR had a marked effect on the response of LC neurons to HA: a reduced percentage of CS LC neurons and decreased CI. In summary, both NTS and LC neurons appear to contain intrinsically CS neurons. CS neurons from the two regions receive different tonic input in slices (excitatory for NTS and inhibitory for LC); however, blocking chemical synaptic input does not affect the CS response in either region. In NTS neurons, gap junction coupling plays a small role in the CS response, but gap junctions play a major role in the chemosensitivity of many LC neurons.

The complete-data model that underlies an Expectation-Maximization (EM) algorithm must have a parameter space that coincides with the parameter space of the observed-data model. Otherwise, maximization of the observed-data log-likelihood will be carried out over a space that does not coincide with the desired parameter space. In some contexts, however, a natural complete-data model may be defined only for parameter values within a subset of the observed-data parameter space. In this paper we discuss situations where this can still be useful if the complete-data model can be viewed as a member of a finite family of complete-data models that have parameter spaces which collectively cover the observed-data parameter space. Such a family of complete-data models defines a family of EM algorithms which together lead to a finite collection of constrained maxima of the observed-data log-likelihood. Maximization of the log-likelihood function over the full parameter space then involves identifying the constrained maximum that achieves the greatest log-likelihood value. Since optimization over a finite collection of candidates is referred to as combinatorial optimization, we refer to such a family of EM algorithms as a combinatorial EM (CEM) algorithm. As well as discussing the theoretical concepts behind CEM algorithms, we discuss strategies for improving the computational efficiency when the number of complete-data models is large. Various applications of CEM algorithms are also discussed, ranging from simple examples that illustrate the concepts, to more substantive examples that demonstrate the usefulness of CEM algorithms in practice.

The sexually dimorphic robust archistriatal nucleus (RA) represents the telencephalic output of the bird song system. Here, we document sex-dependent changes in both the metabolic and neuronal activity of RA during the sensory and sensorimotor phases of song learning. From posthatching day (PHD) 20-63 in males but not females, RA and its input nucleus HVc showed sharp increases in cytochrome oxidase (CO) activity relative to surrounding archistriatum and the underlying shelf, respectively. In urethane-anesthetized birds, during the same period, the spontaneous activity of male RA neurons underwent dramatic changes in firing rate, distribution of interspike intervals, and bursting frequency, compared with other archistriatal cells. At PHD 20-21, RA neurons had extremely slow, irregular firing rates in birds of both sexes. In males, from PHD 30-36, RA neurons increased their firing rates and spiking activity became more regular, and at approximately PHD 38, strong bursts followed by inhibition (which in awake animals is associated with singing) began to be observed. Dual recordings from RA and HVc revealed synchronous bursting, with RA spikes lagging approximately 10 msec behind HVc. We conclude that changes in relative CO activity correlate with changes in spontaneous firing rates within RA and that patterns of RA spontaneous activity exhibit gradual change as birds enter early song and then again for plastic song. The emergence of strong burst patterns in RA occurs later in life than does input from HVc as established by tracer studies or based on observed HVc bursting in young animals.

We investigate the potential of the Large Hadron Collider (LHC) to probe one of the most compelling beyond the Standard Model frameworks---walking technicolor (WTC), involving strong dynamics and having a slowly running (walking) new strong coupling. For this purpose we use recent LHC Run 2 data to explore the full parameter space of the minimal WTC model using dilepton signatures from heavy neutral ${Z}^{\ensuremath{'}}$ and ${Z}^{\ensuremath{'}\ensuremath{'}}$ resonances predicted by the model. This signature is the most promising one for discovery of WTC at the LHC for the low-intermediate values of the $\stackrel{\texttildelow{}}{g}$ coupling---one of the principle parameters of WTC. We have demonstrated complementarity of the dilepton signals from both resonances, established the most up-to-date limit on the WTC parameter space, and provided projections for the LHC potential to probe the WTC parameter space at higher future luminosities and upgraded energy. We have explored the whole four-dimensional parameter space of the model and have found the most conservative limit on the WTC scale ${M}_{A}$ above 3 TeV for the low values of $\stackrel{\texttildelow{}}{g}$, which is significantly higher than previous limits established by the LHC collaborations.

This thesis is concerned with the calibration of disease models in order to inform decisions about public health problems. These models are used to make predictions about the likely outcomes of disease spread (e.g. morbidity and mortality) in a population, given different interventions (e.g. vaccination, screening and treatment). These models necessitate simpli cations of the underlying mechanisms of disease progression, but have become essential tools in areas such as cost-effectiveness research. Although there are many types of models that can be used, all of them involve parameters, which affect the model's outputs, for example, the age of onset of illness, duration of particular health states or disease specifc mortality. Identifying areas of the model's parameter space that are consistent with data is referred to as model calibration. One of the areas of concern when searching a model's parameter space to get a prediction from the model (that best agrees with observed data) is the propagation of uncertainty. If one has a prediction about an outcome, the question of how uncertain we are about this prediction should be raised. In model calibration, the uncertainty we are interested in is uncertainty in parameters[5]. There are many approaches to model calibration with little consensus on the best practice. Additionally, within the current approaches, some signifi cant problems, that one could face when attempting calibration, merit further examination. We highlight this with a literature search. We believe, that many popular approaches have not yet addressed fundamental issues and more research may be needed to tackle these issues. The model calibrations in this thesis are carried out in a Bayesian framework, using Markov chain Monte Carlo (MCMC) techniques. Although the ultimate goal of calibration should be predictions about some quantities of interest, the emphasis of this work is on the techniques and methodology of model calibration in general. Many possible difficulties of using MCMC and some other sampling approaches are presented and discussed. Avenues for additional research are identified.

Mathematical modeling for gene regulative networks (GRNs) provides an effective tool for hypothesis testing in biology. A necessary step in setting up such models is the estimation of model parameters, i.e., an optimization process during which the difference between model output and given experimental data is minimized. This parameter estimation step is often difficult, especially for larger systems due to often incomplete quantitative data, the large size of the parameter space, and non-linearities in system behavior. Addressing the task of parameter estimation, we investigate the influence multiobjectivization can have on the optimization process. On the example of an established model for the segment polarity GRN in Drosophila, we test different multiobjectivization scenarios compared to a singleobjective function proposed earlier for the parameter optimization of the segment polarity network model. Since, instead of a single optimal parameter setting, a set of optimal parameter settings exists for this GRN, the comparison of the different optimization scenarios focuses on the capabilities of the different scenarios to identify optimal parameter settings showing good diversity in the parameter space. By embedding the objective functions in an evolutionary algorithm (EA), we show the superiority of the multiobjective approaches in exploring the model's parameter space.

We present new constraints on spectator axion-U(1) gauge field interactions during inflation using the latest Planck (PR4) and BICEP/Keck 2018 data releases. This model can source tensor perturbations from amplified gauge field fluctuations, driven by an axion rolling for a few e-folds during inflation. The gravitational waves sourced in this way have a strongly scale-dependent (and chiral) spectrum, with potentially visible contributions to large/intermediate scale B-modes of the CMB. We first derive theoretical bounds on the model imposing validity of the perturbative regime and negligible backreaction of the gauge field on the background dynamics. Then, we determine bounds from current CMB observations, adopting a frequentist profile likelihood approach. We study the behaviour of constraints for typical choices of the model's parameters, analyzing the impact of different dataset combinations. We find that observational bounds are competitive with theoretical ones and together they exclude a significant portion of the model's parameter space. We argue that the parameter space still remains large and interesting for future CMB experiments targeting large/intermediate scales B-modes.

This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. The diffusion of information about community resources and their use is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.

We use the Higgs coupling and the muon anomalous magnetic moment measurements to constrain the parameter space of the natural supersymmetry (SUSY) in the Generalized Minimal Supergravity (GmSUGRA) model. We scan the parameter space of the GmSUGRA model with small electroweak fine-tuning measure ($\Delta_{\rm EW} \leq 100$). The parameter space after applying various sparticle mass bounds, Higgs mass bounds, B-physics bounds, the muon magnetic moment constraint, and the Higgs coupling constraint from measurements at HL-LHC, ILC, and CEPC, is shown in the planes of various interesting model parameters and sparticle masses. Our study indicates that the Higgs coupling and muon anomalous magnetic moment measurements can constrain the parameter space effectively. It is shown that $\Delta_{\rm EW}\sim$ 30, consistence with all constraints, and having supersymmetric contributions to the muon anomalous magnetic moment within 1$\sigma$ can be achieved. The precision of $k_b$ and $k_{\tau}$ measurements at CEPC can bound $m_A$ to be above 1.2 TeV and 1.1 TeV respectively. The combination of the Higgs coupling measurement and muon anomalous magnetic moment measurement constrain $\tilde{e}_R$ mass to be in the range from 0.6 TeV to 2 TeV. The range of both $\tilde{e}_L$ and $\tilde{\nu}_e$ masses is 0.4 TeV $\sim$ 1.2 TeV. In all cases, the $\tilde{\chi}_1^0$ mass needs to be small (mostly $\leq$ 400 GeV). The comparison of bounds in the $\tan\beta - m_A$ plane shows that the Higgs coupling measurement is complementary to the direct collider searches for heavy Higgs when constraining the natural SUSY. A few mass spectra in the typical region of parameter space after applying all constraints are shown as well.

History matching of surfactant-polymer flooding using polynomial chaos expansion

The purpose of this study was to determine whether exposure to thyrotropin-releasing hormone (TRH) enhances the excitatory effect of 5-hydroxytryptamine (5-HT) on motoneurons of the dorsal motor nucleus of the vagus (DMV) as described in whole animal studies. For this purpose we used the patch-clamp technique applied to rat brain stem slices. Exposure of DMV motoneurons to concentrations of 5-HT (0.1-3 microM) resulted in a concentration-related increase in spontaneous firing rate. As previously described by Travagli et al. [Am. J. Physiol. 263 (Gastrointest. Liver Physiol. 26): G508-G517, 1992], TRH (1-30 microM) increased action potential firing rate. Indeed, when TRH perfusion increased the firing rate, addition of 5-HT to the perfusing solution exerted no further excitation of the DMV motoneuron, indicating that there was no summation of response. Studies using whole cell current recordings showed a common action of 5-HT and TRH in antagonizing the Ca(2+)-dependent afterhyperpolarizing current (IAHP). Again, interaction studies between TRH and 5-HT indicated no enhancing effect of TRH on 5-HT-induced antagonism of IAHP. In conclusion, our data indicated that the enhancement of 5-HT-induced excitation of DMV motoneurons by TRH described by in vivo rat experiments is not due to an interaction of TRH and 5-HT directly on the DMV motoneuron.