A Multi-Compartment Model for Interneurons in the Dorsal Lateral Geniculate Nucleus

Abstract

GABAergic interneurons (INs) in the dorsal lateral geniculate nucleus (dLGN) shape the information flow from retina to cortex, presumably by controlling the number of visually evoked spikes in geniculate thalamocortical (TC) neurons, and refining their receptive field. The INs exhibit a rich variety of firing patterns: Depolarizing current injections to the soma may induce tonic firing, periodic bursting or an initial burst followed by tonic spiking, sometimes with prominent spike-time adaptation. When released from hyperpolarization, some INs elicit rebound bursts, while others return more passively to the resting potential. A full mechanistic understanding that explains the function of the dLGN on the basis of neuronal morphology, physiology and circuitry is currently lacking. One way to approach such an understanding is by developing a detailed mathematical model of the involved cells and their interactions. Limitations of the previous models for the INs of the dLGN region prevent an accurate representation of the conceptual framework needed to understand the computational properties of this region. We here present a detailed compartmental model of INs using, for the first time, a morphological reconstruction and a set of active dendritic conductances constrained by experimental somatic recordings from INs under several different current-clamp conditions. The model makes a number of experimentally testable predictions about the role of specific mechanisms for the firing properties observed in these neurons. In addition to accounting for the significant features of all experimental traces, it quantitatively reproduces the experimental recordings of the action-potential- firing frequency as a function of injected current. We show how and why relative differences in conductance values, rather than differences in ion channel composition, could account for the distinct differences between the responses observed in two different neurons, suggesting that INs may be individually tuned to optimize network operation under different input conditions.

Functional Effects of Schizophrenia-Linked Genetic Variants on Intrinsic Single-Neuron Excitability: A Modeling Study

“…The increased I SK , in turn, delayed the induction of the next spike and hence resulted in a loss of gain (flattening) in the f-I curve (Figure 3). This finding is in line with previous modeling studies that also have highlighted the role of Ca 2+ -activated K + currents in modulating f-I curves (18,19). …”

Electrodiffusive Model for Astrocytic and Neuronal Ion Concentration Dynamics

“…With the possible exception of the signalling molecule Ca 2+ (see e.g., [28], [29]), ion concentrations are typically assumed to be constant. The effect of ionic diffusion (due to concentration gradients) on the net electrical currents is neglected in standard cable theory, and resistivities (which in reality depend on ion concentrations) are assumed to be constant.…”

Reciprocal inhibition and slow calcium decay in perigeniculate interneurons explain changes of spontaneous firing of thalamic cells caused by cortical inactivation

“…The currents used in the model of feed-forward interneuron, I T , I CAN , I KCa , I K , I Na , I L and I H , were identified by Zhu et al (1999) in the rat thalamus, and recently also found by Halnes et al (2011) in the mouse LGN. We used the appropriate currents from TC and PGN model cells and adjusted parameters to fit to physiological responses of feed-forward interneurons observed by Pape and McCormick (1995).…”

“…The simulated thalamic cells were based on previous physiological and modeling investigations (Huguenard and McCormick 1992; McCormick and Huguenard 1992; Destexhe et al 1994; Pape and McCormick 1995; Zhu and Uhlrich 1997; Zhu et al 1999; Halnes et al 2011) and included all identified currents (listed below in sections describing specific Cell models). All parameters of thalamic model cells were adjusted to fit their responses to injected currents to those observed in the physiological experiments mentioned above.…”

“…All the parameters were manually tested and adjusted with current injections to model cells placed outside the network with no incoming or outgoing connections (which we call in the following dissociated cells ) to fit experimental in vitro activity of neurons (Huguenard and McCormick 1992; McCormick and Huguenard 1992; Pape and McCormick 1995; Zhu and Uhlrich 1997; Zhu et al 1999) or the known modeling results following experimental data (Destexhe et al 1994; Halnes et al 2011). …”