Dynamics and functions of neural circuits depend on interactions mediated by receptors. Therefore, a comprehensive map of receptor organization across cortical regions is needed. In this study, we used in vitro receptor autoradiography to measure the density of 14 neurotransmitter receptor types in 109 areas of macaque cortex. We integrated the receptor data with anatomical, genetic and functional connectivity data into a common cortical space. We uncovered a principal gradient of receptor expression per neuron. This aligns with the cortical hierarchy from sensory cortex to higher cognitive areas. A second gradient, driven by serotonin 5-HT1A receptors, peaks in the anterior cingulate, default mode and salience networks. We found a similar pattern of 5-HT1A expression in the human brain. Thus, the macaque may be a promising translational model of serotonergic processing and disorders. The receptor gradients may enable rapid, reliable information processing in sensory cortical areas and slow, flexible integration in higher cognitive areas.

Recent advances in connectomic and neurophysiological tools make it possible to probe whole-brain mechanisms in the mouse that underlie cognition and behavior. Based on experimental data, we developed a large-scale model of the mouse brain for a cardinal cognitive function called working memory, the brain’s ability to internally hold and process information without sensory input. In the model, interregional connectivity is constrained by mesoscopic connectome data. The density of parvalbumin-expressing interneurons in the model varies systematically across the cortex. We found that the long-range cell type-specific targeting and density of cell classes define working memory representations. A core cortical subnetwork and the thalamus produce distributed persistent activity, and the network exhibits numerous attractor states. Novel cell type-specific graph theory measures predicted the activity patterns and core subnetwork. This work highlights the need for cell type-specific connectomics, and provides a theory and tools to interpret large-scale recordings of brain activity during cognition.

A growing body of evidence suggests that conscious perception of a sensory stimulus triggers an all-or-none activity across multiple cortical areas, a phenomenon called ‘ignition’. In contrast, the same stimulus, when undetected, induces only transient activity. In this work, we report a large-scale model of the macaque cortex based on recently quantified structural connectome data. We use this model to simulate a detection task, and demonstrate how a dynamical bifurcation mechanism produces ignition-like events in the model network. Within this framework, the model predicts that feedforward excitatory transmission is primarily mediated by the fast AMPA receptors to ensure rapid signal propagation from sensory to associative areas. In contrast, a large fraction of the inter-areal feedback projections and local recurrent excitation depend on the slow NMDA receptors, to ensure ignition of distributed frontoparietal activity. Our model predicts, counterintuitively, that fast-responding sensory areas contain a higher ratio of NMDA to AMPA receptors compared to association cortical areas that show slow, sustained activity. We validate this prediction using in-vitro receptor autoradiography data. Finally, we show how this model can account for various behavioral and physiological effects linked to consciousness. Together, these findings clarify the neurophysiological mechanisms of conscious access in the primate cortex and support the concept that gradients of receptor densities along the cortical hierarchy contribute to distributed cognitive functions.

Dopamine is required for working memory, but how it modulates the large-scale cortex is unknown. Here, we report that dopamine receptor density per neuron, measured by autoradiography, displays a macroscopic gradient along the macaque cortical hierarchy. This gradient is incorporated in a connectome-based large-scale cortex model endowed with multiple neuron types. The model captures an inverted U-shaped dependence of working memory on dopamine and spatial patterns of persistent activity observed in over 90 experimental studies. Moreover, we show that dopamine is crucial for filtering out irrelevant stimuli by enhancing inhibition from dendrite-targeting interneurons. Our model revealed that an activity-silent memory trace can be realized by facilitation of inter-areal connections and that adjusting cortical dopamine induces a switch from this internal memory state to distributed persistent activity. Our work represents a cross-level understanding from molecules and cell types to recurrent circuit dynamics underlying a core cognitive function distributed across the primate cortex.