ArticlePLoS computational biology2018
Spike and burst coding in thalamocortical relay cells.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales.PLoS biology · 2024Article
- Stimulus contrast modulates burst activity in the lateral geniculate nucleus.Current research in neurobiology · 2023Article
- Classification of bursting patterns: A tale of two ducks.PLoS computational biology · 2022Review
- Corticothalamic Pathways From Layer 5: Emerging Roles in Computation and Pathology.Frontiers in neural circuits · 2021Review
- Synchrony of Spontaneous Burst Firing between Retinal Ganglion Cells Across Species.Experimental neurobiology · 2020Article
- A Potential Mechanism of Sodium Channel Mediating the General Anesthesia Induced by Propofol.Frontiers in cellular neuroscience · 2020Article
- The Augmentation of Retinogeniculate Communication during Thalamic Burst Mode.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Average firing rate rather than temporal pattern determines metabolic cost of activity in thalamocortical relay neurons.Scientific reports · 2019Article
- Neural Coding With Bursts-Current State and Future Perspectives.Frontiers in computational neuroscience · 2018Review
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA) and the Event-Triggered Covariance (ETC). This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms) and showed a clear distinction between spikes (selective for fluctuations) and bursts (selective for integration). The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (IT) and the cyclic nucleotide modulated h current (Ih). The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two currents. Finally, the model was used to investigate the more realistic "high-conductance state", where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under "standard" conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation.
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