Residual dynamics resolves recurrent contributions to neural computation

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals—that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations

Modelling longitudinal data on respiratory infections to inform health policy

Detecting the start of an outbreak, quantifying its burden, disentangling the contribution of different pathogens and evaluating the effectiveness of an intervention are research questions common to several infectious diseases. The answers to these questions provide the epidemiological understanding to prevent future outbreaks, by informing public health policies such as drug stockpiling, vaccination regimes or non-medical interventions. We investigate the use of statistical models to quantify burden of respiratory disease and evaluate effectiveness of public health interventions, while accounting for the challenges posed by surveillance data. The observational nature of the available information, affected by confounding, makes causal statements difficult. Improvements to routinely employed methodologies are proposed, employing phenomenological models to estimate a counterfactual, i.e. what what would have happened in the absence of a contributing factor or intervention. We apply these methods to different types of studies, to address specific gaps in the literature. S. pneumoniae is the leading cause of respiratory morbidity and mortality globally, especially in young children and in the elderly. To improve the understanding of factors triggering disease progression, we firstly analyse individual-level information about pneumococcal carriage and lower respiratory tract infection with a multi-state model, using data from a cohort study in Thailand. Secondly, we clarify the role of viral coinfection and meteorological conditions in invasive pneumococcal disease (IPD) incidence using English surveillance data. A novel multivariate linear regression model is proposed to estimate the influenza-specific contribution additional to the seasonal IPD burden across age groups. We then quantify the impact of the currently implemented vaccination policy, by estimating the counterfactual of IPD incidence in absence of vaccination. This allows disentangling serotype replacement from the vaccine effect, making use of a synthetic control approach. Finally, an empirical dynamical modelling strategy is employed to quantify the interaction between influenza and pneumococcus. Counterfactual analysis can also be employed to quantify the burden of novel respiratory pathogens. The last application of this approach is to estimate the excess mortality during the the COVID-19 pandemic in England

Neutralizing antibody responses in HIV dual infection: lessons for vaccine design

The development of a safe, effective prophylactic HIV vaccine remains a major global health priority. Stabilized, soluble trimers that mimic the native functional HIV trimer have been developed that elicit strain-specific neutralizing HIV antibodies in animal models, and are currently being evaluated in several human clinical trials. Identifying whether multiple immunogens could be administered to facilitate the broadening of responses represents a pivotal challenge. In this thesis, we characterized the antibody response in individuals infected with multiple HIV strains to inform the development of polyvalent and sequential HIV vaccine regimens. We found that conventional approaches to detect HIV co- and superinfection are confounded by recombination. Therefore, we developed an automated, Bayesian approach to detect superinfection explicitly accounting for recombination. Using simulated and real sequence data, we demonstrated that this approach is sensitive, highly specific, and robust to recombination. Furthermore, analyzing previously published sequence datasets, we identified cases of superinfection that previously went undetected, indicating that superinfection occurs more frequently than previously estimated. We characterized the development of antibodies in five superinfected individuals identified in the CAPRISA 002 acute infection cohort. Specifically, we evaluated whether superinfection re-engaged cross-reactive memory B cells, promoting the development of cross-neutralizing antibodies. By comparing the breadth of the neutralizing antibody response in superinfected individuals to those that typically develop in singly infected individuals, we showed that HIV superinfection was not sufficient to broaden responses. By characterizing the kinetics and specificity of autologous neutralizing antibody responses, we show that responses to the superinfecting viruses failed to efficiently recruit neutralizing memory B cells. Instead, the secondary infection elicited strain-specific, de novo responses. This occurred even though the superinfecting viruses were relatively closely related (from the same subtype). To determine whether the co-exposure to diverse Env antigens favours the development of cross-neutralizing antibodies better than sequential exposure, we characterized the development of neutralizing antibodies in HIV co-infected individuals where several divergent viruses were transmitted prior to seroconversion. We identified three cases of co-infection that encompassed immunological exposure to: (i) two diverse, unlinked Envs, (ii) two related Envs with diversity uniformly distributed over the trimer, and (iii) two diverse but recombined Envs such that clusters of high homology were preserved in the presence of high diversity elsewhere. We found that, like superinfection, co-infection was not sufficient to broaden neutralizing antibody responses. Co-exposure to two HIV Env antigens did not necessarily produce additive or cross-neutralizing antibody responses, and in some cases was subject to immunological interference. This was most evident in the case of co-infection with two related Envs where diversity was uniformly distributed across the Env trimer; in this case neutralizing antibody responses to one variant arose to the near exclusion of responses to the other. However, in the case of co-exposure to diverse Envs but where the trimer apex was conserved in both variants through recombination, potent neutralization of both variants was evident. This was the co-infected participant who developed the broadest neutralizing antibody response, and we show that cross-neutralization was mediated, in part, by trimer apextargeting neutralizing antibodies. In conclusion, we find that HIV superinfection fails to efficiently recruit neutralizing memory B cells and, at best, results in additive nAb responses rather than a synergistic effect leading to cross-neutralization; a distinction that is highly relevant for vaccine design. While sequential immunizations with heterologous Env immunogens may be able to improve the potency of elicited responses, alone, they are unlikely to promote the development of bnAbs. Our observations from cases of co-infection suggests that cocktails of divergent stabilized Env trimers are unlikely to drive the development of cross-neutralizing antibodies, and may be subject to interference. However, the rational design of more similar immunogen cocktails where conserved epitopes are preserved across immunogens may be able to facilitate neutralizing antibodies to these targets, as seen in one individual. Thus, the use of related, stabilized Env trimers with diversity introduced in key regions together with strategies to reduce the immunogenicity of immunodominant, strain-specific epitopes may represent one path to a cross-neutralizing antibody response to multiple Envs within a cocktail

Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data

We develop new methods of Bayesian inference, largely in the context of analysis of neuroscience data. The work is broken into several parts. In the first part, we introduce a novel class of joint probability distributions in which exact inference is tractable. Previously it has been difficult to find general constructions for models in which efficient exact inference is possible, outside of certain classical cases. We identify a class of such models that are tractable owing to a certain "low-rank" structure in the potentials that couple neighboring variables. In the second part we develop methods to quantify and measure information loss in analysis of neuronal spike train data due to two types of noise, making use of the ideas developed in the first part. Information about neuronal identity or temporal resolution may be lost during spike detection and sorting, or precision of spike times may be corrupted by various effects. We quantify the information lost due to these effects for the relatively simple but sufficiently broad class of Markovian model neurons. We find that decoders that model the probability distribution of spike-neuron assignments significantly outperform decoders that use only the most likely spike assignments. We also apply the ideas of the low-rank models from the first section to defining a class of prior distributions over the space of stimuli (or other covariate) which, by conjugacy, preserve the tractability of inference. In the third part, we treat Bayesian methods for the estimation of sparse signals, with application to the locating of synapses in a dendritic tree. We develop a compartmentalized model of the dendritic tree. Building on previous work that applied and generalized ideas of least angle regression to obtain a fast Bayesian solution to the resulting estimation problem, we describe two other approaches to the same problem, one employing a horseshoe prior and the other using various spike-and-slab priors. In the last part, we revisit the low-rank models of the first section and apply them to the problem of inferring orientation selectivity maps from noisy observations of orientation preference. The relevant low-rank model exploits the self-conjugacy of the von Mises distribution on the circle. Because the orientation map model is loopy, we cannot do exact inference on the low-rank model by the forward backward algorithm, but block-wise Gibbs sampling by the forward backward algorithm speeds mixing. We explore another von Mises coupling potential Gibbs sampler that proves to effectively smooth noisily observed orientation maps

Statistical approaches for unraveling the neural code in the visual system

textHere we consider the neural coding problem at two levels of the macaque visual system.First, we analyze single neurons recorded in the lateral intraparietal (LIP) cortex while a monkey performed a perceptual decision-making task. We relate the single-trial responses in LIP to stochastic decision-making processes with latent dynamical models. We compare models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete ``stepping'' dynamics. In contrast to previous findings, roughly three-quarters of the choice-selective neurons we recorded are better described by the stepping model. Second, we introduce a biophysically inspired point process model that explicitly incorporates stimulus-induced changes in synaptic conductance in a dynamical model of neuronal membrane potential. We show that our model provides a tractable model of spike responses in macaque parasol retinal ganglion cells that is both more accurate and more interpretable than the popular generalized linear model. Most importantly, we show that we can accurately infer intracellular synaptic conductances from extracellularly recorded spike trains.Neuroscienc

Investigating Information Flows in Spiking Neural Networks With High Fidelity

The brains of many organisms are capable of a wide variety of complex computations. This capability must be undergirded by a more general purpose computational capacity. The exact nature of this capacity, how it is distributed across the brains of organisms and how it arises throughout the course of development is an open topic of scientific investigation. Individual neurons are widely considered to be the fundamental computational units of brains. Moreover, the finest scale at which large scale recordings of brain activity can be performed is the spiking activity of neurons and our ability to perform these recordings over large numbers of neurons and with fine spatial resolution is increasing rapidly. This makes the spiking activity of individual neurons a highly attractive data modality on which to study neural computation. The framework of information dynamics has proven to be a successful approach towards interrogating the capacity for general purpose computation. It does this by revealing the atomic information processing operations of information storage, transfer and modification. Unfortunately, the study of information flows and other information processing operations from the spiking activity of neurons has been severely hindered by the lack of effective tools for estimating these quantities on this data modality. This thesis remedies this situation by presenting an estimator for information flows, as measured by Transfer Entropy (TE), that operates in continuous time on event-based data such as spike trains. Unlike the previous approach to the estimation of this quantity, which discretised the process into time bins, this estimator operates on the raw inter-spike intervals. It is demonstrated to be far superior to the previous discrete-time approach in terms of consistency, rate of convergence and bias. Most importantly, unlike the discrete-time approach, which requires a hard tradeoff between capturing fine temporal precision or history effects occurring over reasonable time intervals, this estimator can capture history effects occurring over relatively large intervals without any loss of temporal precision. This estimator is applied to developing dissociated cultures of cortical rat neurons, therefore providing the first high-fidelity study of information flows on spiking data. It is found that the spatial structure of the flows locks in to a significant extent. at the point of their emergence and that certain nodes occupy specialised computational roles as either transmitters, receivers or mediators of information flow. Moreover, these roles are also found to lock in early. In order to fully understand the structure of neural information flows, however, we are required to go beyond pairwise interactions, and indeed multivariate information flows have become an important tool in the inference of effective networks from neuroscience data. These are directed networks where each node is connected to a minimal set of sources which maximally reduce the uncertainty in its present state. However, the application of multivariate information flows to the inference of effective networks from spiking data has been hampered by the above-mentioned issues with preexisting estimation techniques. Here, a greedy algorithm which iteratively builds a set of parents for each target node using multivariate transfer entropies, and which has already been well validated in the context of traditional discretely sampled time series, is adapted to use in conjunction with the newly developed estimator for event-based data. The combination of the greedy algorithm and continuous-time estimator is then validated on simulated examples for which the ground truth is known. The new capabilities in the estimation of information flows and the inference of effective networks on event-based data presented in this work represent a very substantial step forward in our ability to perform these analyses on the ever growing set of high resolution, large scale recordings of interacting neurons. As such, this work promises to enable substantial quantitative insights in the future regarding how neurons interact, how they process information, and how this changes under different conditions such as disease