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 to adjust, fine-tune and validate a three-compartment TCR model cell (Destexhe et al. 1998, accession number 279). Three currents were added: an h-current (Destexhe et al. 1993,1996, accession number 3343), a high-threshold calcium current and a calcium- activated potassium current (Huguenard & McCormick 1994, accession number 3808). 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. Finally, the model was used to in the more realistic “high-conductance state” (Destexhe et al. 2001, accession number 8115), while being stimulated with a Poisson input (Brette et al. 2007, Vogels & Abbott 2005, accession number 83319), 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.
Model Type: Neuron or other electrically excitable cell
Region(s) or Organism(s): Thalamus
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell
Currents: I L high threshold; I K,Ca; I h; I T low threshold
Model Concept(s): Bursting; Information transfer; Rebound firing; Sensory coding
Simulation Environment: NEURON
Implementer(s): Zeldenrust, Fleur [fleurzeldenrust at gmail.com]
Zeldenrust F, Chameau P, Wadman WJ. (2018). Spike and burst coding in thalamocortical relay cells. PLoS computational biology. 14 [PubMed]
Destexhe A, Bal T, McCormick DA, Sejnowski TJ. (1996). Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices. Journal of neurophysiology. 76 [PubMed]
Mccormick DA, Huguenard JP. (1994). Electrophysiology of the Neuron: An Interactive Tutorial.
Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience. 107 [PubMed]
Brette R et al. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. Journal of computational neuroscience. 23 [PubMed]
Vogels TP, Abbott LF. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience. 25 [PubMed]
Destexhe A, Neubig M, Ulrich D, Huguenard J. (1998). Dendritic low-threshold calcium currents in thalamic relay cells. The Journal of neuroscience : the official journal of the Society for Neuroscience. 18 [PubMed]
Destexhe A, Babloyantz A, Sejnowski TJ. (1993). Ionic mechanisms for intrinsic slow oscillations in thalamic relay neurons. Biophysical journal. 65 [PubMed]