Neurobehavioral meaning of pupil size

Neuron, Journal Year: 2024, Volume and Issue: 112(20), P. 3381 - 3395

Published: June 25, 2024

Pupil size is a widely used metric of brain state. It one the few signals originating from that can be readily monitored with low-cost devices in basic science, clinical, and home settings. is, therefore, important to investigate generate well-defined theories related specific interpretations this metric. What exactly does it tell us about brain? Pupils constrict response light dilate during darkness, but also controls pupil irrespective luminosity. fluctuations resulting ongoing "brain states" are as arousal, what pupil-linked arousal how should interpreted neural, cognitive, computational terms? Here, we discuss some recent findings these issues. We identify open questions propose answer them through combination tasks, neurocomputational models, neurophysiological probing interconnected loops causes consequences size.

Language: Английский

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Foundation model of neural activity predicts response to new stimulus types

Nature, Journal Year: 2025, Volume and Issue: 640(8058), P. 470 - 477

Published: April 9, 2025

Abstract The complexity of neural circuits makes it challenging to decipher the brain’s algorithms intelligence. Recent breakthroughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding computational objectives and coding. However, is difficult for such generalize beyond their training distribution, limiting utility. emergence foundation 1 trained on vast datasets has introduced a new artificial intelligence paradigm with remarkable generalization capabilities. Here we collected large amounts activity from visual cortices multiple mice model predict neuronal responses arbitrary natural videos. This generalized minimal successfully predicted across various stimulus domains, as coherent motion noise patterns. Beyond response prediction, also anatomical cell types, dendritic features connectivity within MICrONS functional connectomics dataset 2 . Our work crucial step towards building brain. As neuroscience accumulates larger, multimodal datasets, will reveal statistical regularities, enable rapid adaptation tasks accelerate research.

Language: Английский

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From pre-processing to advanced dynamic modeling of pupil data

Behavior Research Methods, Journal Year: 2023, Volume and Issue: 56(3), P. 1376 - 1412

Published: June 22, 2023

The pupil of the eye provides a rich source information for cognitive scientists, as it can index variety bodily states (e.g., arousal, fatigue) and processes attention, decision-making). As pupillometry becomes more accessible popular methodology, researchers have proposed techniques analyzing data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in size over with stimulus series, continuous behavioral outcome measures, or other participants' traces. We first introduce pupillometry, its neural underpinnings, relation between measurements oculomotor behaviors blinks, saccades), stress importance understanding what is being measured be inferred from pupillary activity. Next, discuss possible pre-processing steps, contexts which they may necessary. Finally, turn analytic techniques, including regression-based approaches, time-warping, phase clustering, detrended fluctuation analysis, recurrence quantification analysis. Assumptions these examples scientific questions each address, are outlined, references key papers software packages. Additionally, provide detailed code tutorial steps through figures this paper. Ultimately, contend insights gained constrained by analysis used, offer means generate novel taking into account understudied spectro-temporal relationships signal signals interest.

Language: Английский

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Decision-making dynamics are predicted by arousal and uninstructed movements

Cell Reports, Journal Year: 2024, Volume and Issue: 43(2), P. 113709 - 113709

Published: Jan. 26, 2024

During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize can be predicted by externally observable measures, such as uninstructed movements and arousal. Here, using computational modeling visual auditory data from mice, we uncovered lawful relationships between transitions strategic states arousal movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, find animals fluctuate minutes-long optimal, sub-optimal, disengaged states. Optimal state epochs are intermediate levels, reduced variability, pupil diameter movement. Our results demonstrate behaviors predict optimal suggest mice regulate their

Language: Английский

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Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales

PLoS Biology, Journal Year: 2024, Volume and Issue: 22(5), P. e3002614 - e3002614

Published: May 14, 2024

The processing of sensory information, even at early stages, is influenced by the internal state animal. Internal states, such as arousal, are often characterized relating neural activity to a single “level” defined behavioral indicator pupil size. In this study, we expand understanding arousal-related modulations in systems uncovering multiple timescales dynamics and their relationship activity. Specifically, observed robust coupling between spiking mouse dorsolateral geniculate nucleus (dLGN) thalamus across spanning few seconds several minutes. Throughout all these timescales, 2 distinct modes—individual tonic spikes tightly clustered bursts spikes—preferred opposite phases dynamics. This multi-scale reveals from those captured size per se, locomotion, eye movements. Furthermore, persisted during viewing naturalistic movie, where it contributed differences encoding visual information. We conclude that dLGN under simultaneous influence processes associated with occurring over broad range timescales.

Language: Английский

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Functional connectomics spanning multiple areas of mouse visual cortex

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2021, Volume and Issue: unknown

Published: July 29, 2021

Abstract To understand the brain we must relate neurons’ functional responses to circuit architecture that shapes them. Here, present a large connectomics dataset with dense calcium imaging of millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher areas (VISrl, VISal VISlm) an awake mouse viewing natural movies synthetic stimuli. The data were co-registered volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells 0.5 billion synapses. Subsequent proofreading subset this volume yielded reconstructions include complete dendritic trees as well local inter-areal axonal projections map up thousands cell-to-cell connections per neuron. release open-access resource scientific community including set tools facilitate retrieval downstream analysis. In accompanying papers describe our findings using provide comprehensive structural characterization cortical cell types 1–3 most detailed synaptic level connectivity diagram column date 2 , uncovering unique cell-type specific inhibitory motifs can be linked gene expression 4 . Functionally, identify new computational principles how information is integrated across space 5 characterize novel neuronal invariances 6 bring structure function together decipher general principle wires excitatory within 7, 8

Language: Английский

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Deep learning-driven characterization of single cell tuning in primate visual area V4 unveils topological organization

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: May 13, 2023

Abstract Deciphering the brain’s structure-function relationship is key to understanding neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, classic example primate neocortex organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence across higher-level cortex debated. A hurdle identifying difficulty characterizing complex nonlinear tuning, especially high-dimensional inputs. Here, we asked whether area V4, mid-level macaque visual system, organized into columns. We combined large-scale linear probe recordings deep learning methods systematically characterize tuning >1,200 V4 silico synthesis most exciting images (MEIs), followed by vivo verification. found that MEIs single exhibited features like textures, shapes, or even high-level attributes such as eye-like structures. Neurons recorded on same silicon probe, inserted orthogonal surface, were selective spatial features, expected from columnar quantified this finding human psychophysics measuring MEI similarity non-linear embedding space, learned contrastive loss. Moreover, selectivity population was clustered, suggesting form distinct functional groups shared feature selectivity, reminiscent cell types. These closely mirrored maps units artificial vision systems, hinting at encoding principles between biological vision. Our findings provide evidence types may constitute universal organizing neocortex, simplifying cortex’s complexity simpler circuit motifs which perform canonical computations.

Language: Английский

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A chromatic feature detector in the retina signals visual context changes

eLife, Journal Year: 2024, Volume and Issue: 13

Published: Oct. 4, 2024

The retina transforms patterns of light into visual feature representations supporting behaviour. These are distributed across various types retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has difficult to link potentially nonlinear transformations natural inputs specific ethological purposes. Here, we discover a selectivity chromatic contrast an RGC type that allows detection changes context. We trained convolutional neural network (CNN) on large-scale functional recordings responses mouse movies, then used this search silico for stimuli maximally excite distinct RGCs. This procedure predicted centre colour opponency transient suppressed-by-contrast (tSbC) RGCs, cell function is being debated. confirmed experimentally these indeed responded very selectively Green-OFF, UV-ON contrasts. was characteristic transitions from ground sky scene, as might be elicited by head or eye movements horizon. Because tSbC performed best among all at reliably detecting transitions, suggest role providing contextual information (i.e. ground) necessary selection appropriate behavioural other stimuli, such looming objects. Our work showcases how combination experiments with computational modelling discovering novel stimulus identifying their potential relevance.

Language: Английский

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Decoding the brain: From neural representations to mechanistic models

Cell, Journal Year: 2024, Volume and Issue: 187(21), P. 5814 - 5832

Published: Oct. 1, 2024

Language: Английский

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Towards a Foundation Model of the Mouse Visual Cortex

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: March 24, 2023

The complexity of neural circuits makes it challenging to decipher the brain’s algorithms intelligence. Recent break-throughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding computational objectives and coding. However, these struggle generalize beyond their training distribution, limiting utility. emergence foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts activity from visual cortices multiple mice model predict neuronal responses arbitrary natural videos. This generalized minimal successfully predicted across various stimulus domains, such as coherent motion noise patterns. It could also be adapted tasks prediction, predicting anatomical cell types, dendritic features, connectivity within MICrONS functional connectomics dataset. Our work is crucial step toward building models. As neuroscience accumulates larger, multi-modal will uncover statistical regularities, enabling rapid adaptation accelerating research.

Language: Английский

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