ArticleJournal of computational neuroscience2011
Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells.
Article in Journal of computational neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 62 citations in OpenAlex.
- Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs.eLife · 2024Article
- Transcranial direct current stimulation of cerebellum alters spiking precision in cerebellar cortex: A modeling study of cellular responses.PLoS computational biology · 2021Article
- A model of on/off transitions in neurons of the deep cerebellar nuclei: deciphering the underlying ionic mechanisms.Journal of mathematical neuroscience · 2021Article
- Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions.PLoS computational biology · 2020Article
- A general method to generate artificial spike train populations matching recorded neurons.Journal of computational neuroscience · 2020Article
- Role of cerebellar GABAergic dysfunctions in the origins of essential tremor.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Article
- Complex Electroresponsive Dynamics in Olivocerebellar Neurons Represented With Extended-Generalized Leaky Integrate and Fire Models.Frontiers in computational neuroscience · 2019Article
- Robust transmission of rate coding in the inhibitory Purkinje cell to cerebellar nuclei pathway in awake mice.PLoS computational biology · 2017Article
- Cerebellar Purkinje Cells Generate Highly Correlated Spontaneous Slow-Rate Fluctuations.Frontiers in neural circuits · 2017Article
- Determinants of rebound burst responses in rat cerebellar nuclear neurons to physiological stimuli.The Journal of physiology · 2016Article
- Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo.PloS one · 2016Article
- Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue.Frontiers in cellular neuroscience · 2016Review
- Stochastic Synchronization in Purkinje Cells with Feedforward Inhibition Could Be Studied with Equivalent Phase-Response Curves.Journal of mathematical neuroscience · 2015Article
- Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses.PLoS computational biology · 2015Article
- A Recurrent Mutation in CACNA1G Alters Cav3.1 T-Type Calcium-Channel Conduction and Causes Autosomal-Dominant Cerebellar Ataxia.American journal of human genetics · 2015Article
- Signal processing by T-type calcium channel interactions in the cerebellum.Frontiers in cellular neuroscience · 2013Review
- The neuronal code(s) of the cerebellum.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2013Review
- Modeling the generation of output by the cerebellar nuclei.Neural networks : the official journal of the International Neural Network Society · 2013Review
- Gain control of synaptic response function in cerebellar nuclear neurons by a calcium-activated potassium conductance.Cerebellum (London, England) · 2013Article
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5 authors at 4 institutions in 4 countries.
Funding
Abstract
Significant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit complex intrinsic properties. In particular, DCN neurons exhibit a range of rebound spiking properties following hyperpolarizing current injection, raising the question how this could contribute to signal processing in behaving animals. Computer modeling presents an ideal tool to investigate how intrinsic voltage-gated conductances in DCN neurons could generate the heterogeneous firing behavior observed, and what input conditions could result in rebound responses. To enable such an investigation we built a compartmental DCN neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.
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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.