arXiv subjects

Matthew Dowling

Publications and source records attributed to Matthew Dowling.

At least 19 recordsLinked to original sources

arXiv2026

Memory by Design: Probabilistic Sequence Layers

We introduce the \emph{design-model framework}: a way to derive efficient recurrent sequence maps from explicit assumptions about memory. A design model writes evidence into memory by exact Bayesian filtering; a query- dependent readout produces a predictive distribution whose mean is the layer output. In our linear-Gaussian instantiation, the \emph{Bayesian Layer} propagates both a mean and a covariance: the covariance tracks uncertainty over stored associations, steering writes toward uncertain directions, attenuating gains as evidence accumulates, and preserving confident memories. The same framework unifies several sub-quadratic recurrences: linear attention, GLA, and Mamba-2/SSD are exact filters under a latent-input design model, whereas DeltaNet and related Delta-rule models are covariance-reset reductions of the Bayesian Layer's design model. Restoring covariance propagation yields closed-form predictions for retrieval dynamics, which we verify empirically, and improves robustness beyond the training regime in controlled collision studies, learned associative recall, and the Zoology MQAR benchmark. Training from scratch on WikiText-103 under matched state budgets lowers perplexity on associative-recall hits. Distilling Bayesian Layers into a pretrained 340M Gated DeltaNet improves RULER long-context retrieval over a matched-compute control, at a 2.5--2.7\% held-out perplexity cost.

stat.ML↗

arXiv2026

Integrative neurocybernetic modeling in the era of large-scale neuroscience

Large-scale neuroscience is generating rich datasets across animals, brain areas and behavioral contexts, yet our modeling efforts remains fragmented across isolated experiments. We argue that understanding behavior requires integrative neurocybernetic models: understandable dynamical models that capture the closed-loop coupling of brain, body and environment, treat the brain as a controller pursuing latent objectives, represent structured variation across scales, and scale to heterogeneous datasets. Such models shift the goal from predicting neural recordings in isolation to inferring the organizing principles that govern neural and behavioral dynamics. We outline a practical route toward this goal by combining nonlinear state-space models and meta-dynamical extensions with scalable inference, knowledge distillation, mixed open- and closed-loop training, and connectomics-informed architectures. By pooling complementary constraints from recordings, behavior, perturbations and anatomy, integrative neurocybernetic models can provide statistical amplification, few-shot generalization, and mechanistic insight into shared dynamical structure, individual variation, and the control objectives that govern behavior. This agenda offers a model-centric path from fragmented data to a mechanistic science of how brains produce behavior.

q-bio.NC↗

arXiv2024

Meta-Dynamical State Space Models for Integrative Neural Data Analysis

Learning shared structure across environments facilitates rapid learning and adaptive behavior in neural systems. This has been widely demonstrated and applied in machine learning to train models that are capable of generalizing to novel settings. However, there has been limited work exploiting the shared structure in neural activity during similar tasks for learning latent dynamics from neural recordings. Existing approaches are designed to infer dynamics from a single dataset and cannot be readily adapted to account for statistical heterogeneities across recordings. In this work, we hypothesize that similar tasks admit a corresponding family of related solutions and propose a novel approach for meta-learning this solution space from task-related neural activity of trained animals. Specifically, we capture the variabilities across recordings on a low-dimensional manifold which concisely parametrizes this family of dynamics, thereby facilitating rapid learning of latent dynamics given new recordings. We demonstrate the efficacy of our approach on few-shot reconstruction and forecasting of synthetic dynamical systems, and neural recordings from the motor cortex during different arm reaching tasks.

stat.ML↗

arXiv2024

Real-Time Machine Learning Strategies for a New Kind of Neuroscience Experiments

Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. This gap restricts the scope of experiments vital for advancing both fundamental and clinical neuroscience. Recent advances in real-time machine learning technologies, particularly in analyzing neural time series as nonlinear stochastic dynamical systems, are beginning to bridge this gap. These technologies enable immediate interpretation of and interaction with neural systems, offering new insights into neural computation. However, several significant challenges remain. Issues such as slow convergence rates, high-dimensional data complexities, structured noise, non-identifiability, and a general lack of inductive biases tailored for neural dynamics are key hurdles. Overcoming these challenges is crucial for the full realization of real-time neural data analysis for the causal investigation of neural computation and advanced perturbation based brain machine interfaces. In this paper, we provide a comprehensive perspective on the current state of the field, focusing on these persistent issues and outlining potential paths forward. We emphasize the importance of large-scale integrative neuroscience initiatives and the role of meta-learning in overcoming these challenges. These approaches represent promising research directions that could redefine the landscape of neuroscience experiments and brain-machine interfaces, facilitating breakthroughs in understanding brain function, and treatment of neurological disorders.

q-bio.NC↗

arXiv2024

eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling

State-space graphical models and the variational autoencoder framework provide a principled apparatus for learning dynamical systems from data. State-of-the-art probabilistic approaches are often able to scale to large problems at the cost of flexibility of the variational posterior or expressivity of the dynamics model. However, those consolidations can be detrimental if the ultimate goal is to learn a generative model capable of explaining the spatiotemporal structure of the data and making accurate forecasts. We introduce a low-rank structured variational autoencoding framework for nonlinear Gaussian state-space graphical models capable of capturing dense covariance structures that are important for learning dynamical systems with predictive capabilities. Our inference algorithm exploits the covariance structures that arise naturally from sample based approximate Gaussian message passing and low-rank amortized posterior updates -- effectively performing approximate variational smoothing with time complexity scaling linearly in the state dimensionality. In comparisons with other deep state-space model architectures our approach consistently demonstrates the ability to learn a more predictive generative model. Furthermore, when applied to neural physiological recordings, our approach is able to learn a dynamical system capable of forecasting population spiking and behavioral correlates from a small portion of single trials.

stat.ML↗

arXiv2023

Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains

Latent Gaussian process (GP) models are widely used in neuroscience to uncover hidden state evolutions from sequential observations, mainly in neural activity recordings. While latent GP models provide a principled and powerful solution in theory, the intractable posterior in non-conjugate settings necessitates approximate inference schemes, which may lack scalability. In this work, we propose cvHM, a general inference framework for latent GP models leveraging Hida-Mat\'ern kernels and conjugate computation variational inference (CVI). With cvHM, we are able to perform variational inference of latent neural trajectories with linear time complexity for arbitrary likelihoods. The reparameterization of stationary kernels using Hida-Mat\'ern GPs helps us connect the latent variable models that encode prior assumptions through dynamical systems to those that encode trajectory assumptions through GPs. In contrast to previous work, we use bidirectional information filtering, leading to a more concise implementation. Furthermore, we employ the Whittle approximate likelihood to achieve highly efficient hyperparameter learning.

q-bio.NC↗

arXiv2023

Real-Time Variational Method for Learning Neural Trajectory and its Dynamics

Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. However, despite the potential of real time alternatives to give immediate feedback to experimentalists, and enhance experimental design, they have received markedly less attention. In this work, we introduce the exponential family variational Kalman filter (eVKF), an online recursive Bayesian method aimed at inferring latent trajectories while simultaneously learning the dynamical system generating them. eVKF works for arbitrary likelihoods and utilizes the constant base measure exponential family to model the latent state stochasticity. We derive a closed-form variational analogue to the predict step of the Kalman filter which leads to a provably tighter bound on the ELBO compared to another online variational method. We validate our method on synthetic and real-world data, and, notably, show that it achieves competitive performance

stat.ML↗

arXiv2021

Hida-Mat\'ern Kernel

We present the class of Hida-Mat\'ern kernels, which is the canonical family of covariance functions over the entire space of stationary Gauss-Markov Processes. It extends upon Mat\'ern kernels, by allowing for flexible construction of priors over processes with oscillatory components. Any stationary kernel, including the widely used squared-exponential and spectral mixture kernels, are either directly within this class or are appropriate asymptotic limits, demonstrating the generality of this class. Taking advantage of its Markovian nature we show how to represent such processes as state space models using only the kernel and its derivatives. In turn this allows us to perform Gaussian Process inference more efficiently and side step the usual computational burdens. We also show how exploiting special properties of the state space representation enables improved numerical stability in addition to further reductions of computational complexity.

stat.ML↗

arXiv2021

Three-loop corrections to the muon and heavy quark decay rates

Recently, $O(\alpha^3)$ corrections to the muon decay rate and $O(\alpha_s^3)$ to heavy quark decays have been determined by Fael, Sch\"onwald, and Steinhauser. This is the first such perturbative improvement of these important quantities in more than two decades. We reveal and explain a symmetry pattern in these new corrections, and confirm the most technically difficult parts of their evaluation.

hep-ph↗

arXiv2020

Non-parametric generalized linear model

A fundamental problem in statistical neuroscience is to model how neurons encode information by analyzing electrophysiological recordings. A popular and widely-used approach is to fit the spike trains with an autoregressive point process model. These models are characterized by a set of convolutional temporal filters, whose subsequent analysis can help reveal how neurons encode stimuli, interact with each other, and process information. In practice a sufficiently rich but small ensemble of temporal basis functions needs to be chosen to parameterize the filters. However, obtaining a satisfactory fit often requires burdensome model selection and fine tuning the form of the basis functions and their temporal span. In this paper we propose a nonparametric approach for jointly inferring the filters and hyperparameters using the Gaussian process framework. Our method is computationally efficient taking advantage of the sparse variational approximation while being flexible and rich enough to characterize arbitrary filters in continuous time lag. Moreover, our method automatically learns the temporal span of the filter. For the particular application in neuroscience, we designed priors for stimulus and history filters useful for the spike trains. We compare and validate our method on simulated and real neural spike train data.

stat.ML↗

arXiv2017

Two-loop binding corrections to the electron gyromagnetic factor

We compute corrections to the gyromagnetic factor of an electron bound in a hydrogen-like ion at order alpha^2(Z alpha)^5. This result removes a major uncertainty in predictions for silicon and carbon ions, used to determine the atomic mass of the electron.

physics.atom-ph↗

arXiv2016

QCD corrections to vector boson pair production in gluon fusion including interference effects with off-shell Higgs at the LHC

We compute next-to-leading order (NLO) QCD corrections to the production of two massive electroweak bosons in gluon fusion. We consider both the prompt production process $gg \to VV$ and the production mediated by an exchange of an s-channel Higgs boson, $gg \to H^* \to V V$. We include final states with both on- and off-shell vector bosons with leptonic decays. The gluonic production of vector bosons is a loop-induced process, including both massless and massive quarks in the loop. For $gg \to ZZ$ production, we obtain the NLO QCD corrections to the massive loops through an expansion around the heavy top limit. This approximation is valid below the top production threshold, giving a broad range of invariant masses between the Higgs production and the top production thresholds in which our results are valid. We explore the NLO QCD effects in $gg \to ZZ$ focusing, in particular, on the interference between prompt and Higgs-mediated processes. We find that the QCD corrections to the interference are large and similar in size to the corrections to both the signal and the background processes. At the same time, we observe that corrections to the interference change rapidly with the four-lepton invariant mass in the region around the ZZ production threshold. We also study the interference effects in $gg \to WW$ production where, due to technical limitations, we only consider contributions of massless loops. We find that the QCD corrections to the interference in this case are somewhat larger than those for either the signal or the background.

hep-ph↗

arXiv2015

Production of two Z-bosons in gluon fusion in the heavy top quark approximation

We compute QCD radiative corrections to the continuum production of a pair of Z-bosons in the annihilation of two gluons. We only consider the contribution of the top quark loops and we treat them assuming that $m_t$ is much larger than any other kinematic invariant in the problem. We estimate the QCD corrections to $pp \to ZZ$ using the first non-trivial term in the expansion in the inverse top quark mass and we compare them to QCD corrections of the signal process, $pp \to H \to ZZ$.

hep-ph↗

arXiv2014

Michel decay spectrum for a muon bound to a nucleus

The spectrum of electrons from muons decaying in an atomic bound state is significantly modified by their interaction with the nucleus. Somewhat unexpectedly, its first measurement, at the Canadian laboratory TRIUMF, differed from basic theory. We show, using a combination of techniques developed in atomic, nuclear, and high-energy physics, that radiative corrections eliminate the discrepancy. In addition to solving that outstanding problem, our more precise predictions are potentially useful for interpreting future high-statistics muon experiments that aim to search for exotic interactions at $10^{-16}$ sensitivity.

hep-ph↗

arXiv2013

Top-quark Pair Production in a Running Mass Scheme

Between the Tevatron and LHC, top-quark physics is now becoming an area for precision physics. This has lead to an increase in theoretical activity to match the experimental accuracy of top anti-top production. We discuss the difficulty in properly defining the top-quark mass as measured by experiments and present results for differential distributions of top-quark pair production in a running mass scheme. The use of such a scheme shows better convergence in the perturbative expansion and improves the scale dependence as opposed to the typical on-shell scheme.

hep-ph↗

arXiv2013

Differential distributions for top-quark hadro-production with a running mass

We take a look at how the differential distributions for top-quark production are affected by changing to the running mass scheme. Specifically we consider the transverse momentum, rapidity and pair-invariant mass distributions at NLO for the top-quark mass in the MSbar scheme. It is found that, similar to the total cross section, the perturbative expansion converges faster and the scale dependence improves using the mass in the MSbar scheme as opposed to the on-shell scheme. We also update the analysis for the total cross section using the now available full NNLO contribution.

hep-ph↗

arXiv2011

Bounds on majoron emission from muon to electron conversion experiments

In models where lepton number is considered to be a spontaneously-broken global symmetry a massless Goldstone boson, the majoron (J), appears. We describe a procedure to explore the muon-electron-majoron coupling using the results from mu-e conversion search experiments. To accomplish that, we determine how the energy spectrum of the muon decay into an electron and a majoron is modified by binding effects in a muonic atom. We find that the future mu-to-e conversion experiments may be able to produce bounds on the mu->eJ rate which are comparable with the present ones from direct searches.

hep-ph↗

arXiv2010

Magnetic moment of a bound electron

Theoretical predictions underlying determinations of the fine structure constant alpha and the electron-to-proton mass ratio m_e/m_p are reviewed, with the emphasis on the bound electron magnetic anomaly g-2. The theory of the interaction of hydrogen-like ions with a magnetic field is discussed. The status of efforts aimed at the determination of O(alpha (Z alpha)^5) and O(alpha^2 (Z alpha)^5) corrections to the g factor is presented. The reevaluation of analogous corrections to the Lamb shift and the hyperfine splitting is summarized.

hep-ph↗