sampling

TACTIC: Tuberculosis Active Case Tracking via Interpersonal Connections

PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) remains the leading infectious cause of death worldwide. Interruption of transmission is the most effective strategy to reduce incident infections, yet current approaches often fail to reach individuals for timely testing and treatment. This study addresses that gap by leveraging social networks to identify individuals at highest risk of transmitting TB, specifically, people who use drugs (PWUD). We will evaluate respondent-driven sampling (RDS), a peer7 based community recruitment strategy, to identify TB cases among PWUD and the household contacts (HHCs) of those with TB disease (RDS-TB) in Kampala, Uganda. Conducting this work in a high-prevalence setting such as Kampala where our team has established expertise allows us to overcome recruitment challenges common in settings in the United States while generating findings that are directly translatable. This is particularly relevant given that higher TB prevalence and larger outbreaks in the United States have been associated with the use of methamphetamine, heroin, and crack/cocaine, drugs that we will study. In Aim 1, we will compare the effectiveness and reach of RDS-TB with a traditional clinic-based index case HHC approach for TB case finding. We will screen 2,000 PWUD and their HHCs, estimate the number needed to screen to identify one case of TB disease, and compare the demographic and network characteristics of RDS-TB recruits with clinic-based HHCs. Whole genome sequencing will be used to characterize transmission dynamics. In Aim 2, we will compare the yield of individual and combined TB diagnostic strategies for community-based active case finding. Participants will undergo chest radiography with computer-aided detection, tongue swab testing for TB nucleic acid amplification tests (NAAT), and sputum testing for NAAT and mycobacterial culture. We will identify the minimal combination of tests needed to meet World Health Organization target product profile thresholds for screening. In Aim 3, we will define the conditions under which RDS-based screening can effectively interrupt TB transmission. We will develop an agent-based model informed by social network data from individuals with and without TB, incorporating drug use patterns and demographic characteristics. This project will generate a practical, scalable roadmap for social network–based TB active case finding in high28 risk communities. The approach will be readily adaptable to settings in the United States and will inform strategies to interrupt transmission and advance progress toward TB elimination, in alignment with the NIH Strategic Plan for TB Research.

Calcium signaling in MR1-dependent presentation of Mycobacterium tuberculosis antigens

Project Summary The fundamental role of the immune system is to detect self from non-self. The detection and elimination of microbial infection is critical for human survival. One challenge to the immune system is infection from an intracellular microbe because the microbe masks its presence in a host cell. One strategy of the immune system to detect microbes is the sampling of different kinds of antigens, such as peptides, lipids and glycolipids, by antigen presenting molecules. A fundamentally unique arm of the immune system is MR1, which is an antigen presenting molecule that is intracellular, ubiquitously expressed across tissues, and detects small molecules derived from microbial metabolism. These features suggest that MR1 is poised to detect intracellular microbes. MR1 presents antigens to MR1-restricted T cells. These T cells are highly prevalent in the lungs and can kill infected cells. Because MR1 presents small molecule antigens and adopts an intracellular distribution, the mechanisms governing MR1 sampling of the intracellular environment are distinct from other antigen presenting molecules. These mechanisms remain unknown. Our over-arching hypothesis is that intracellular calcium signaling is important for MR1 antigen presentation. We use Mycobacterium tuberculosis (Mtb) as a model for intracellular infection and have identified calcium-sensitive trafficking proteins and calcium channels important for MR1 antigen presentation. Aim 1 of this study will determine the mechanism of two-pore channel 1 in MR1- dependent antigen presentation, with a focus on endoplasmic reticulum-endosome contact sites. Aim 2 will determine the role of specific calcium-sensitive Synaptotagmins and their binding partners. Aim 3 will determine the mechanism behind augmented MR1 antigen presentation following modulation of the of the cystic fibrosis transmembrane conductance regulator. Successful completion of these Aims has the potential to lead to new MR1-based immunotherapies.

Environmental sampling for the fungal pathogen Coccidioides spp. in New Mexico

PROJECT SUMMARY/ABSTRACT Coccidioidomycosis (also known as Valley fever) is a fungal disease endemic to the arid and semi-arid portions of the United States. Due to its alarming health impacts, it has recently been deemed as one of the fungal diseases of highest concern by the World Health Organization. Disease cases continue to rise, causing increasing concern and warranting further understanding of this disease. Though New Mexico has been considered endemic to the disease since the 1940s, few cases are reported in the state each year, suggesting cases may be going vastly underreported. Indeed, recent epidemiologic models suggest New Mexico is likely underreporting cases, which may be a result of low disease awareness in the state or a lack of understanding what populations are at risk. Meanwhile, the neighboring state of Arizona reports the highest number of cases in the country, despite similarities in climate and ecology to New Mexico. In general, very little is known about the health burden of coccidioidomycosis and the geographical distribution of the causative fungal pathogen, Coccidioides spp., in New Mexico. Interestingly, both species of Coccidioides are likely endemic to New Mexico and hybridization of the species may occur. This, in combination with a variety of different ecosystems across the state, makes New Mexico an ideal location for studying the ecology of Coccidioides spp. The objective of our proposal is to generate preliminary data to gain a better understanding of the geographical distribution of Coccidioides spp. in New Mexico, including any regions where hybridization of the species may be occurring. To achieve this, we will collect soil samples throughout New Mexico to: (1) identify what ecosystems are conducive for the growth of Coccidioides and each Coccidioides species in New Mexico and (2) assess whether locations directly surrounding New Mexico’s five largest population centers (Albuquerque, Las Cruces, Rio Rancho, Santa Fe, and Roswell) are endemic to Coccidioides spp. The positive impacts of our proposal are an understanding of what populations are at risk for contracting this disease, where future disease surveillance efforts should be targeted in the state, and where future soil samples should be collected to further explore the genomics and phenotypes of Coccidioides spp. and potential for hybridization. This will help us achieve our long-term goal: to understand the ecology and endemicity of Coccidioides spp. to increase disease awareness, mitigate the negative health impacts from coccidioidomycosis, and ultimately protect the health of all Americans.

A recurrent network model of planning predicts hippocampal replay and human behavior

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as `rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by -- and in turn adaptively affect -- prefrontal dynamics.

The development of visual experience

Vision and visual cognition is experience-dependent with likely multiple sensitive periods, but we know very little about statistics of visual experience at the scale of everyday life and how they might change with development. By traditional assumptions, the world at the massive scale of daily life presents pretty much the same visual statistics to all perceivers. I will present an overview our work on ego-centric vision showing that this is not the case. The momentary image received at the eye is spatially selective, dependent on the location, posture and behavior of the perceiver. If a perceiver’s location, possible postures and/or preferences for looking at some kinds of scenes over others are constrained, then their sampling of images from the world and thus the visual statistics at the scale of daily life could be biased. I will present evidence with respect to both low-level and higher level visual statistics about the developmental changes in the visual input over the first 18 months post-birth.

A recurrent network model of planning explains hippocampal replay and human behavior

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as 'rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by - and in turn adaptively affect - prefrontal dynamics.

Dynamic endocrine modulation of the nervous system

Sex hormones are powerful neuromodulators of learning and memory. In rodents and nonhuman primates estrogen and progesterone influence the central nervous system across a range of spatiotemporal scales. Yet, their influence on the structural and functional architecture of the human brain is largely unknown. Here, I highlight findings from a series of dense-sampling neuroimaging studies from my laboratory designed to probe the dynamic interplay between the nervous and endocrine systems. Individuals underwent brain imaging and venipuncture every 12-24 hours for 30 consecutive days. These procedures were carried out under freely cycling conditions and again under a pharmacological regimen that chronically suppresses sex hormone production. First, resting state fMRI evidence suggests that transient increases in estrogen drive robust increases in functional connectivity across the brain. Time-lagged methods from dynamical systems analysis further reveals that these transient changes in estrogen enhance within-network integration (i.e. global efficiency) in several large-scale brain networks, particularly Default Mode and Dorsal Attention Networks. Next, using high-resolution hippocampal subfield imaging, we found that intrinsic hormone fluctuations and exogenous hormone manipulations can rapidly and dynamically shape medial temporal lobe morphology. Together, these findings suggest that neuroendocrine factors influence the brain over short and protracted timescales.

Sampling the environment with body-brain rhythms

Since Darwin, comparative research has shown that most animals share basic timing capacities, such as the ability to process temporal regularities and produce rhythmic behaviors. What seems to be more exclusive, however, are the capacities to generate temporal predictions and to display anticipatory behavior at salient time points. These abilities are associated with subcortical structures like basal ganglia (BG) and cerebellum (CE), which are more developed in humans as compared to nonhuman animals. In the first research line, we investigated the basic capacities to extract temporal regularities from the acoustic environment and produce temporal predictions. We did so by adopting a comparative and translational approach, thus making use of a unique EEG dataset including 2 macaque monkeys, 20 healthy young, 11 healthy old participants and 22 stroke patients, 11 with focal lesions in the BG and 11 in the CE. In the second research line, we holistically explore the functional relevance of body-brain physiological interactions in human behavior. Thus, a series of planned studies investigate the functional mechanisms by which body signals (e.g., respiratory and cardiac rhythms) interact with and modulate neurocognitive functions from rest and sleep states to action and perception. This project supports the effort towards individual profiling: are individuals’ timing capacities (e.g., rhythm perception and production), and general behavior (e.g., individual walking and speaking rates) influenced / shaped by body-brain interactions?

Seeing the world through moving photoreceptors - binocular photomechanical microsaccades give fruit fly hyperacute 3D-vision

To move efficiently, animals must continuously work out their x,y,z positions with respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes’ optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes’ whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsaccades enable Drosophila superresolution three-dimensional vision and proposes neural computations for accurately predicting these flies’ depth-perception dynamics, limits, and visual behaviors.

Do Capuchin Monkeys, Chimpanzees and Children form Overhypotheses from Minimal Input? A Hierarchical Bayesian Modelling Approach

Abstract concepts are a powerful tool to store information efficiently and to make wide-ranging predictions in new situations based on sparse data. Whereas looking-time studies point towards an early emergence of this ability in human infancy, other paradigms like the relational match to sample task often show a failure to detect abstract concepts like same and different until the late preschool years. Similarly, non-human animals have difficulties solving those tasks and often succeed only after long training regimes. Given the huge influence of small task modifications, there is an ongoing debate about the conclusiveness of these findings for the development and phylogenetic distribution of abstract reasoning abilities. Here, we applied the concept of “overhypotheses” which is well known in the infant and cognitive modeling literature to study the capabilities of 3 to 5-year-old children, chimpanzees, and capuchin monkeys in a unified and more ecologically valid task design. In a series of studies, participants themselves sampled reward items from multiple containers or witnessed the sampling process. Only when they detected the abstract pattern governing the reward distributions within and across containers, they could optimally guide their behavior and maximize the reward outcome in a novel test situation. We compared each species’ performance to the predictions of a probabilistic hierarchical Bayesian model capable of forming overhypotheses at a first and second level of abstraction and adapted to their species-specific reward preferences.

Frontal circuit specialisations for information search and decision making

During primate evolution, prefrontal cortex (PFC) expanded substantially relative to other cortical areas. The expansion of PFC circuits likely supported the increased cognitive abilities of humans and anthropoids to sample information about their environment, evaluate that information, plan, and decide between different courses of action. What quantities do these circuits compute as information is being sampled towards and a decision is being made? And how can they be related to anatomical specialisations within and across PFC? To address this, we recorded PFC activity during value-based decision making using single unit recording in non-human primates and magnetoencephalography in humans. At a macrocircuit level, we found that value correlates differ substantially across PFC subregions. They are heavily shaped by each subregion’s anatomical connections and by the decision-maker’s current locus of attention. At a microcircuit level, we found that the temporal evolution of value correlates can be predicted using cortical recurrent network models that temporally integrate incoming decision evidence. These models reflect the fact that PFC circuits are highly recurrent in nature and have synaptic properties that support persistent activity across temporally extended cognitive tasks. Our findings build upon recent work describing economic decision making as a process of attention-weighted evidence integration across time.

Does human perception rely on probabilistic message passing?

The idea that perception in humans relies on some form of probabilistic computations has become very popular over the last decades. It has been extremely difficult however to characterize the extent and the nature of the probabilistic representations and operations that are manipulated by neural populations in the human cortex. Several theoretical works suggest that probabilistic representations are present from low-level sensory areas to high-level areas. According to this view, the neural dynamics implements some forms of probabilistic message passing (i.e. neural sampling, probabilistic population coding, etc.) which solves the problem of perceptual inference. Here I will present recent experimental evidence that human and non-human primate perception implements some form of message passing. I will first review findings showing probabilistic integration of sensory evidence across space and time in primate visual cortex. Second, I will show that the confidence reports in a hierarchical task reveal that uncertainty is represented both at lower and higher levels, in a way that is consistent with probabilistic message passing both from lower to higher and from higher to lower representations. Finally, I will present behavioral and neural evidence that human perception takes into account pairwise correlations in sequences of sensory samples in agreement with the message passing hypothesis, and against standard accounts such as accumulation of sensory evidence or predictive coding.

Understanding Perceptual Priors with Massive Online Experiments

One of the most important questions in psychology and neuroscience is understanding how the outside world maps to internal representations. Classical psychophysics approaches to this problem have a number of limitations: they mostly study low dimensional perpetual spaces, and are constrained in the number and diversity of participants and experiments. As ecologically valid perception is rich, high dimensional, contextual, and culturally dependent, these impediments severely bias our understanding of perceptual representations. Recent technological advances—the emergence of so-called “Virtual Labs”— can significantly contribute toward overcoming these barriers. Here I present a number of specific strategies that my group has developed in order to probe representations across a number of dimensions. 1) Massive online experiments can increase significantly the amount of participants and experiments that can be carried out in a single study, while also significantly diversifying the participant pool. We have developed a platform, PsyNet, that enables “experiments as code,” whereby the orchestration of computer servers, recruiting, compensation of participants, and data management is fully automated and every experiment can be fully replicated with one command line. I will demonstrate how PsyNet allows us to recruit thousands of participants for each study with a large number of control experimental conditions, significantly increasing our understanding of auditory perception. 2) Virtual lab methods also enable us to run experiments that are nearly impossible in a traditional lab setting. I will demonstrate our development of adaptive sampling, a set of behavioural methods that combine machine learning sampling techniques (Monte Carlo Markov Chains) with human interactions and allow us to create high-dimensional maps of perceptual representations with unprecedented resolution. 3) Finally, I will demonstrate how the aforementioned methods can be applied to the study of perceptual priors in both audition and vision, with a focus on our work in cross-cultural research, which studies how perceptual priors are influenced by experience and culture in diverse samples of participants from around the world.

Neural mechanisms of active vision in the marmoset monkey

Human vision relies on rapid eye movements (saccades) 2-3 times every second to bring peripheral targets to central foveal vision for high resolution inspection. This rapid sampling of the world defines the perception-action cycle of natural vision and profoundly impacts our perception. Marmosets have similar visual processing and eye movements as humans, including a fovea that supports high-acuity central vision. Here, I present a novel approach developed in my laboratory for investigating the neural mechanisms of visual processing using naturalistic free viewing and simple target foraging paradigms. First, we establish that it is possible to map receptive fields in the marmoset with high precision in visual areas V1 and MT without constraints on fixation of the eyes. Instead, we use an off-line correction for eye position during foraging combined with high resolution eye tracking. This approach allows us to simultaneously map receptive fields, even at the precision of foveal V1 neurons, while also assessing the impact of eye movements on the visual information encoded. We find that the visual information encoded by neurons varies dramatically across the saccade to fixation cycle, with most information localized to brief post-saccadic transients. In a second study we examined if target selection prior to saccades can predictively influence how foveal visual information is subsequently processed in post-saccadic transients. Because every saccade brings a target to the fovea for detailed inspection, we hypothesized that predictive mechanisms might prime foveal populations to process the target. Using neural decoding from laminar arrays placed in foveal regions of area MT, we find that the direction of motion for a fixated target can be predictively read out from foveal activity even before its post-saccadic arrival. These findings highlight the dynamic and predictive nature of visual processing during eye movements and the utility of the marmoset as a model of active vision. Funding sources: NIH EY030998 to JM, Life Sciences Fellowship to JY

Rhythmic Attentional Sampling: Spatial selection and beyond

Choosing, fast and slow: Implications of prioritized-sampling models for understanding automaticity and control

The idea that behavior results from a dynamic interplay between automatic and controlled processing underlies much of decision science, but has also generated considerable controversy. In this talk, I will highlight behavioral and neural data showing how recently-developed computational models of decision making can be used to shed new light on whether, when, and how decisions result from distinct processes operating at different timescales. Across diverse domains ranging from altruism to risky choice biases and self-regulation, our work suggests that a model of prioritized attentional sampling and evidence accumulation may provide an alternative explanation for many phenomena previously interpreted as supporting dual process models of choice. However, I also show how some features of the model might be taken as support for specific aspects of dual-process models, providing a way to reconcile conflicting accounts and generating new predictions and insights along the way.

Emergence of long time scales in data-driven network models of zebrafish activity

How can neural networks exhibit persistent activity on time scales much larger than allowed by cellular properties? We address this question in the context of larval zebrafish, a model vertebrate that is accessible to brain-scale neuronal recording and high-throughput behavioral studies. We study in particular the dynamics of a bilaterally distributed circuit, the so-called ARTR, including hundreds neurons. ARTR exhibits slow antiphasic alternations between its left and right subpopulations, which can be modulated by the water temperature, and drive the coordinated orientation of swim bouts, thus organizing the fish spatial exploration. To elucidate the mechanism leading to the slow self-oscillation, we train a network graphical model (Ising) on neural recordings. Sampling the inferred model allows us to generate synthetic oscillatory activity, whose features correctly capture the observed dynamics. A mean-field analysis of the inferred model reveals the existence several phases; activated crossing of the barriers in between those phases controls the long time scales present in the network oscillations. We show in particular how the barrier heights and the nature of the phases vary with the water temperature.

Childhood as a solution to explore-exploit tensions

I argue that the evolution of our life history, with its distinctively long, protected human childhood allows an early period of broad hypothesis search and exploration, before the demands of goal-directed exploitation set in. This cognitive profile is also found in other animals and is associated with early behaviours such as neophilia and play. I relate this developmental pattern to computational ideas about explore-exploit trade-offs, search and sampling, and to neuroscience findings. I also present several lines of new empirical evidence suggesting that young human learners are highly exploratory, both in terms of their search for external information and their search through hypothesis spaces. In fact, they are sometimes more exploratory than older learners and adults.

Multi-resolution Multi-task Gaussian Processes: London air pollution

Poor air quality in cities is a significant threat to health and life expectancy, with over 80% of people living in urban areas exposed to air quality levels that exceed World Health Organisation limits. In this session, I present a multi-resolution multi-task framework that handles evidence integration under varying spatio-temporal sampling resolution and noise levels. We have developed both shallow Gaussian Process (GP) mixture models and deep GP constructions that naturally handle this evidence integration, as well as biases in the mean. These models underpin our work at the Alan Turing Institute towards providing spatio-temporal forecasts of air pollution across London. We demonstrate the effectiveness of our framework on both synthetic examples and applications on London air quality. For further information go to: https://www.turing.ac.uk/research/research-projects/london-air-quality. Collaborators: Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang and Mark Girolami.

Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference

Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots, and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory-inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization, as well as stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset, and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of awake monkey recordings. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function — fast sampling-based inference — and predict further properties of these motifs that can be tested in future experiments

Inferring Brain Rhythm Circuitry and Burstiness

Bursts in gamma and other frequency ranges are thought to contribute to the efficiency of working memory or communication tasks. Abnormalities in bursts have also been associated with motor and psychiatric disorders. The determinants of burst generation are not known, specifically how single cell and connectivity parameters influence burst statistics and the corresponding brain states. We first present a generic mathematical model for burst generation in an excitatory-inhibitory (EI) network with self-couplings. The resulting equations for the stochastic phase and envelope of the rhythm’s fluctuations are shown to depend on only two meta-parameters that combine all the network parameters. They allow us to identify different regimes of amplitude excursions, and to highlight the supportive role that network finite-size effects and noisy inputs to the EI network can have. We discuss how burst attributes, such as their durations and peak frequency content, depend on the network parameters. In practice, the problem above follows the a priori challenge of fitting such E-I spiking networks to single neuron or population data. Thus, the second part of the talk will discuss a novel method to fit mesoscale dynamics using single neuron data along with a low-dimensional, and hence statistically tractable, single neuron model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous ‘pools’ of neurons, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using an E-I network of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived. We show that both single-neuron and connectivity parameters can be adequately recovered from simulated data.

Controlled sampling of non-equilibrium brain dynamics: modeling and estimation from neuroimaging signals

Bernstein Conference 2024

Modeling Decision-Making in Trajectory Extrapolation Tasks: Comparing Random Sampling Model and Multi-Layer Perceptron Approaches

Bernstein Conference 2024

Auxiliary neurons in optimized recurrent neural circuit speed up sampling-based probabilistic inference

COSYNE 2022

Neural network size balances representational drift and flexibility during Bayesian sampling

COSYNE 2022

Neural network size balances representational drift and flexibility during Bayesian sampling

COSYNE 2022

Fitting normative neural sampling hypothesis models to neuronal response data

COSYNE 2023

Sampling-based representation of uncertainty during hippocampal theta sequences

COSYNE 2023

Bridging sampling methods with attractor dynamics in spiking head direction networks

COSYNE 2025

Neural sampling in a balanced spiking network with internally generated variability

COSYNE 2025

Repeat at-home sampling of gamified behavioural tasks and wearable dry-EEG can match the quality of lab-based systems

Distinguishing integration and non-integration accounts of sequential sampling in perceptual decision-making

FENS Forum 2024

Waveform sampling-based fluorescence lifetime fiber photometry (FLiP) measurements

FENS Forum 2024

Population geometry enables fast sampling in spiking neural networks

Neuromatch 5