Computational Circuit Mechanisms Underlying Thalamic Control of Attention

Abstract

SummaryThe thalamus is a key brain structure engaged in attentional functions, such as selectively amplifying task-relevant signals of one sensory modality while filtering distractors of another. To investigate computational mechanisms of attentional modulation, we developed a biophysically grounded thalamic reticular circuit model, comprising excitatory thalamocortical and inhibitory reticular neurons, which captures characteristic neurophysiological observations from the alert behaving animals. Top-down attentional control inputs onto reticular neurons effectively modulate thalamic gain and enhance downstream readout, to improve performance across detection, discrimination, and cross-modal task paradigms. Heterogeneity of thalamic responses plays an essential role in downstream decoding during attentional modulation. Dynamical systems analysis explains why reticular neurons are an especially potent site for top-down control, as implicated by experiments. Perturbation analysis reveals excitation-inhibition ratio as an effective parameter governing thalamic processing. These findings establish experimentally testable circuit mechanisms for attentional control in thalamus, with implications for distributed neural control of cognitive processing.

Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task

“…Microstate analysis leverages the excellent temporal resolution of EEG [64] and a meta-criterion on global field power [89], favoring the highest signal-to-noise ratio [16]. The proposed computational circuit mechanisms [37] have presented selective attention [15] as cortical excitability alterations by the thalamus [43] acting as a "spotlight, " which is postulated for the error-related brain state changes [44]. Here, the microstate approach for a brain state correlates of the response [73] to error has a crucial a priori assumption that only one spatial topography map entirely defines the relevant global state of the brain at each moment in time and the residuals are considered noise.…”

Specific connectivity optimizes learning in thalamocortical loops

“…This overlooks experimental findings showing convergent inputs from multiple cortical areas to individual thalamic neurons, 97 rich heterogeneity in wiring across cortical layers that project to/from the thalamus, 7 and diversity in the membrane properties across thalamic neurons, 98 which may place additional constraints on computation in CTC loops. Finally, the thalamic reticular nucleus mediates local inhibition in the thalamus and alters its dynamical regime, 99 which we did not consider in our models.…”

Thalamic control of sensory enhancement and sleep spindle properties in a biophysical model of thalamoreticular microcircuitry

“…Although it is, to the best of our knowledge, the most detailed model of its kind created so far, it is only a first step. Future studies will further refine the thalamoreticular model to take into account newly observed cellular and synaptic properties (Li et al, 2020; Martinez-Garcia et al, 2020), and explore thalamoreticular contributions to other functions, such as attention and the generation of the alpha rhythm (Ahrens et al, 2015; Chen et al, 2016; Gu et al, 2021; Makinson and Huguenard, 2015; Nestvogel and McCormick, 2021), when considered in the broader context of the thalamocortical loop.…”