Biology subjects

Schartner, M.

Publications and source records attributed to Schartner, M..

7 recordsLinked to original sources

bioRxiv2025

How learned expectations shape brain-wide responses

Expectations stemming from prior knowledge have long been known to influence stimulus processing and decision-making. Empowered by brain-wide recordings during a two-choice sensory decision-making task from the International Brain Laboratory1, we sought to identify the form of expectation-based modulations and how they influence ongoing computations. To discern where, when, and how prior knowledge about the stimulus - known to be stored in the brain over the inter-trial period2 - influences brain activity over the arc from stimulus processing to choice selection and action generation, we disentangled the encoding of the highly correlated stimulus, choice, and prior variables as sensory processors, choice/integration processors, and movement generators. We examined which brain regions were modulated by prior expectations and at which point in the task, and characterized whether those modulations take the form of biasing initial activations or response gains. We found that the activities of choice/integrators and movement generators were influenced by prior expectation, while purely stimulus processors were not significantly modulated by prior expectation. In addition, we found that expectation-based modulation takes the form of both initial activity bias and gain modulation in integration/choice computations and in movement generators. We further find that the brain-wide recordings reveal an emergent simplicity: A few-parameter mechanistic neural circuit model captures the population dynamics of stimulus response, integration/choice processing, and action initiation, as well as their modulations by the prior. After fitting to physiology, the models behavior predicts the psychometric and chronometric behavior of mice with no additional parameters. It reveals that the larger bias and gain modulations of movement initiators relative to integration/choice in the data and model is inherited by amplifying integrator/choice inputs rather than being an additional expectation-based effect. Collectively, our results characterize in fine spatiotemporal detail and brain-wide breadth the mechanisms and loci where prior expectations influence cognition, while the novel synthesis between large-scale electrophysiology and mechanistic modeling reveals an organizing simplicity behind perceptual decision making across the brain.

neuroscience↗

bioRxiv2025

Spatially distributed and regionally unbound cellular resolution brain-wide processing loops in mice

Until recently, brain activity could be examined either globally, through regional averaging, or locally, at cellular resolution--but not both at once. As a result, regions were characterized as functionally homogeneous units while single neurons were often characterized as heterogeneously and randomly tuned. Here, we overcome this long-standing divide by leveraging the unprecedented density of International Brain Laboratory electrophysiological recordings in mice performing a sensorimotor decision-making task. Computationally integrating these recordings and ordering neurons by functional similarity reveals directly to the eye the full temporal arc of computations unfurling over the phases of a trial and across the brain at cellular and millisecond resolution, providing a dynamical map of the architecture of brain-wide computation. We report that one of the largest shares of brain-wide variance is explained not by task-related sensory responses, integration, or decision related signals, but by neurons encoding internally generated, trial-timed broadcast sequences that carry contextual information and are biased toward hippocampal and entorhinal involvement. We find that individual neurons reliably specialize in interpretable functions, yet neurons with similar functional roles are widely dispersed across regions. Each region contains cells spanning nearly the full repertoire of response types, rendering functional tuning largely unpredictable from cytoarchitectural boundaries or spatial location alone. In particular, even primary visual cortex contains a broad diversity of response types beyond stimulus-locked responses. The dominant discernible organizational distinction is instead between cortical and subcortical processing. Subcortical neurons carry the bulk of internal computations in expert mice, including integration, decision-making, and movement initiation. These patterns support a model of broadcast rather than staged computation, in which signals are widely shared and transformed rather than serially processed. Finally, we find that despite widespread regional mixing, representations at the single neuron level are not randomly mixed. Neurons combine specific features in highly correlated groupings, contradicting the hypothesis of random mixed selectivity at the cellular level. Together, our findings suggest a revised architecture of brain computation: typically interpretable and non-random selectivity at the level of single neurons, coupled to continual brain-wide broadcast rather than staged processing, in which internally structured activity dominates neural dynamics and scaffolds cognition.

neuroscience↗

bioRxiv2024

Interactive data exploration websites for large-scale electrophysiology

Methodological advances in neuroscience have enabled the collection of massive datasets which demand innovative approaches for scientific communication. Existing platforms for data storage lack intuitive tools for data exploration, limiting our ability to interact effectively with these brain-wide datasets. We introduce two public websites: (Data and Atlas) developed for the International Brain Laboratory which provide access to millions of behavioral trials and hundreds of thousands of individual neurons. These interfaces allow users to discover both the raw and processed brain-wide data released by the IBL at the scale of the whole brain, individual sessions, trials, and neurons. By hosting these data interfaces as websites they are available cross-platform with no installation. By releasing each sites code as a modular open-source framework, other researchers can easily develop their own web interfaces and explore their own data. As neuroscience datasets continue to expand, customizable web interfaces offer a glimpse into a future of streamlined data exploration and act as blueprints for future tools.

neuroscience↗

bioRxiv2023

A Brain-Wide Map of Neural Activity during Complex Behaviour

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with prior expectations to initiate movements and thereby make decisions. It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here, we report a comprehensive set of recordings from 621733 neurons across 139 mice in 12 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 699 Neuropixels probe insertions covering 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encoded key task variables. Representations of visual stimuli appeared transiently in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of mid- and hindbrain regions that also encoded choices. Neural responses correlated with motor action almost everywhere in the brain. Responses to reward delivery and consumption versus negative feedback were also widespread. This publicly available dataset represents an unprecedented resource for understanding how computations distributed across and within brain areas drive behaviour.

neuroscience↗

bioRxiv2023

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.

neuroscience↗

bioRxiv2020

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently. In this work, we improve on these methods (particularly in the regime of few training labels) by leveraging the rich spatiotemporal structures pervasive in behavioral video -- specifically, the spatial statistics imposed by physical constraints (e.g., paw to elbow distance), and the temporal statistics imposed by smoothness from frame to frame. We propose a probabilistic graphical model built on top of deep neural networks, Deep Graph Pose (DGP), to leverage these useful spatial and temporal constraints, and develop an efficient structured variational approach to perform inference in this model. The resulting semi-supervised model exploits both labeled and unlabeled frames to achieve significantly more accurate and robust tracking while requiring users to label fewer training frames. In turn, these tracking improvements enhance performance on downstream applications, including robust unsupervised segmentation of behavioral "syllables," and estimation of interpretable "disentangled" low-dimensional representations of the full behavioral video. Open source code is available at https://github.com/paninski-lab/deepgraphpose.

animal behavior and cognition↗

bioRxiv2019

Neural correlates of the DMT experience as assessed via multivariate EEG

Studying transitions in and out of the altered state of consciousness caused by intravenous (IV) N,N-Dimethyltryptamine (DMT - a fast-acting tryptamine psychedelic) offers a safe and powerful means of advancing knowledge on the neurobiology of conscious states. Here we sought to investigate the effects of IV DMT on the power spectrum and signal diversity of human brain activity (6 female, 7 male) recorded via multivariate EEG, and plot relationships between subjective experience, brain activity and drug plasma concentrations across time. Compared with placebo, DMT markedly reduced oscillatory power in the alpha and beta bands and robustly increased spontaneous signal diversity. Time-referenced analyses revealed close relationships between changes in various aspects of subjective experience and changes in brain activity. Importantly, the emergence of oscillatory activity within the delta and theta frequency bands was found to correlate with the peak of the experience, and particularly its eyes-closed visual component. These findings highlight marked changes in oscillatory activity and signal diversity with DMT that parallel broad and specific components of the relevant subjective experience and thus further our understanding of the neurobiological underpinnings of immersive states of consciousness.

neuroscience↗