Transition between functional regimes in an integrate-and-fire network model of the thalamus

Abstract

The thalamus is a key brain element in the processing of sensory information. During the sleep and awake states, this brain area is characterized by the presence of two distinct dynamical regimes: in the sleep state activity is dominated by spindle oscillations (7 − 15 Hz) weakly affected by external stimuli, while in the awake state the activity is primarily driven by external stimuli. Here we develop a simple and computationally efficient model of the thalamus that exhibits two dynamical regimes with different information-processing capabilities, and study the transition between them. The network model includes glutamatergic thalamocortical (TC) relay neurons and GABAergic reticular (RE) neurons described by adaptive integrate-and-fire models in which spikes are induced by either depolarization or hyperpolarization rebound. We found a range of connectivity conditions under which the thalamic network composed by these neurons displays the two aforementioned dynamical regimes. Our results show that TC-RE loops generate spindle-like oscillations and that a minimum level of clustering (i.e. local connectivity density) in the RE-RE connections is necessary for the coexistence of the two regimes. We also observe that the transition between the two regimes occurs when the external excitatory input on TC neurons (mimicking sensory stimulation) is large enough to cause a significant fraction of them to switch from hyperpolarization-rebound-driven firing to depolarization-driven firing. Overall, our model gives a novel and clear description of the role that the two types of neurons and their connectivity play in the dynamical regimes observed in the thalamus, and in the transition between them. These results pave the way for the development of efficient models of the transmission of sensory information from periphery to cortex.

Thalamocortical spectral transmission relies on balanced input strengths

“…We investigated in silico the properties of thalamocortical information transmission through our local network model of the thalamus receiving external stimuli [34] and projecting to the primary cortex receiving stimulus-unrelated excitatory inputs [38] ( Figure 1A). We computed the cortical LFP Γ (t) and the thalamic LFP T (t) [39] ( Figure 1B, see Methods for details) as main outputs of the system.…”

“…Here we summarize the main features of the model; we refer to [14] for further details. The network is an extension of a thalamic network model introduced in [34] extended to include a network model of a related cortical area following methods of [40,38].…”

“…Specifically, we computed the power spectral density of the thalamic LFP and the TC population firing rate, observing that the θ component is much less pronounced in the latter than in the overall LFP. This suggests that part of the discrepancy between thalamic and cortical PSD in the θ range [12] is that only a weak component of thalamic spindle oscillations is transmitted to the cortex, as these oscillations are stronger in the RE than in the TC population [34]. Still, even considering the actual input to the cortex, the δ band is transmitted in a much more faithful way than the θ oscillations ( Figure 4B).…”

“…Moreover, RE network structure shows recurrent connections with probability p = 0.04 on which we add a degree of clustering by starting from a ring network and then randomly rewiring with probability 0.25. This is necessary for the onset of sustained thalamic oscillations [34]. The model includes thalamocortical afferents by considering synaptic connections from TC neurons to cortical excitatory and inhibitory populations.…”

“…To capture the mechanisms underlying the relationship between spectral content in the thalamus (both input driven and internally generated) and in the cortex, we first checked whether our model reproduced the lack of spindle oscillations in the cortex experimentally observed [12]. We compared the thalamic PSD T and the associated cortical PSD Γ from the respective LFPs both without external stimuli and during external stimulation, (see Methods and [34] (Figure 2A). When introducing external inputs, the gamma peak becomes stronger [40] in the cortex, while a secondary beta peak appears in the thalamus ( Figure 2B).…”