RevieweLife2022
Neural mechanisms underlying the temporal organization of naturalistic animal behavior.
Review in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Stochastic modeling of long-legged ant A. gracilipes locomotion in laboratory experiments.PLoS computational biology · 2026Article
- Alterations in the Multivariate Organization of Plasma Fatty Acid Profiles and Spontaneous Behavior in an AlClBiology · 2026Article
- Article
- AccelerometerBehavior: R Package for Classifying Ungulate Behaviors Into Three States.Ecology and evolution · 2025Article
- Deep neural networks and stochastic methods for cognitive modeling of rat behavioral dynamics inCognitive neurodynamics · 2025Article
- Following the robot's lead: Predicting human and robot movement from EEG in a motor learning HRI task.iScience · 2025Article
- Inferring internal states across mice and monkeys using facial features.Nature communications · 2025Article
- An auditory cortical-striatal circuit supports sound-triggered timing to predict future events.PLoS biology · 2025Article
- Hierarchical competing inhibition circuits govern motor stability in C. elegans.Nature communications · 2025Article
- Hierarchical behavior control by a single class of interneurons.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Olfactory bulb tracks breathing rhythms and place in freely behaving mice.bioRxiv : the preprint server for biology · 2024Article
- Hand-Jaw Coordination as Mice Handle Food Is Organized around Intrinsic Structure-Function Relationships.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information.Nature communications · 2024Article
- Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.Journal of neural engineering · 2024Article
- A tonically active master neuron modulates mutually exclusive motor states at two timescales.Science advances · 2024Article
- Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.bioRxiv : the preprint server for biology · 2024Article
- Neurodynamical Computing at the Information Boundaries of Intelligent Systems.Cognitive computation · 2024Article
- A Cortico-Striatal Circuit for Sound-Triggered Prediction of Reward Timing.bioRxiv : the preprint server for biology · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.