ArticlePLoS computational biology2019
Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed.
- Awake alpha bursting emerges as the dynamic working state in a lateral geniculate thalamocortical cell model.PLoS computational biology · 2026Article
- Neuronal excitability and parameter variability in the Hodgkin-Huxley model.PLoS computational biology · 2026Article
- Local thalamic interneurons drive spindle termination and enable sleep-dependent learning.bioRxiv : the preprint server for biology · 2025Article
- Biophysical modeling of thalamic reticular nucleus subpopulations and their differential contribution to spindle dynamics.iScience · 2025Article
- Closed-Loop Connectivity Best Supports Angular Tuning and Sleep Dynamics in a Biophysical Thalamocortical Circuit Model.bioRxiv : the preprint server for biology · 2025Article
- Ventral posterolateral and ventral posteromedial thalamocortical neurons have distinct physiological properties.Journal of neurophysiology · 2023Article
- Article
- Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics.Cell reports · 2023Article
- A universal workflow for creation, validation, and generalization of detailed neuronal models.Patterns (New York, N.Y.) · 2023Article
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- Thalamic control of sensory processing and spindles in a biophysical somatosensory thalamoreticular circuit model of wakefulness and sleep.Cell reports · 2023Article
- Can neuron modeling constrained by ultrafast imaging data extract the native function of ion channels?Frontiers in computational neuroscience · 2023Article
- The role of thalamic group II mGlu receptors in health and disease.Neuronal signaling · 2022Review
- Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons.PLoS computational biology · 2022Article
- Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE.Frontiers in neuroinformatics · 2022Article
- Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons.PloS one · 2022Article
- Benchmarking of tools for axon length measurement in individually-labeled projection neurons.PLoS computational biology · 2021Article
- A new reduced-morphology model for CA1 pyramidal cells and its validation and comparison with other models using HippoUnit.Scientific reports · 2021Article
- Corticothalamic Pathways From Layer 5: Emerging Roles in Computation and Pathology.Frontiers in neural circuits · 2021Review
- Computational Evidence for a Competitive Thalamocortical Model of Spikes and Spindle Activity in Rolandic Epilepsy.Frontiers in computational neuroscience · 2021Article
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9 authors.
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Abstract
Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.
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