Open access resource for cellular-resolution analyses of corticocortical connectivity in the marmoset monkey

Understanding the principles of neuronal connectivity requires tools for efficient quantification and visualization of large datasets. The primate cortex is particularly challenging due to its complex mosaic of areas, which in many cases lack clear boundaries. Here, we introduce a resource that allows exploration of results of 143 retrograde tracer injections in the marmoset neocortex. Data obtained in different animals are registered to a common stereotaxic space using an algorithm guided by expert delineation of histological borders, allowing accurate assignment of connections to areas despite interindividual variability. The resource incorporates tools for analyses relative to cytoarchitectural areas, including statistical properties such as the fraction of labeled neurons and the percentage of supragranular neurons. It also provides purely spatial (parcellation-free) data, based on the stereotaxic coordinates of 2 million labeled neurons. This resource helps bridge the gap between high-density cellular connectivity studies in rodents and imaging-based analyses of human brains

Bifurcation study of a neural field competition model with an application to perceptual switching in motion integration.

Perceptual multistability is a phenomenon in which alternate interpretations of a fixed stimulus are perceived intermittently. Although correlates between activity in specific cortical areas and perception have been found, the complex patterns of activity and the underlying mechanisms that gate multistable perception are little understood. Here, we present a neural field competition model in which competing states are represented in a continuous feature space. Bifurcation analysis is used to describe the different types of complex spatio-temporal dynamics produced by the model in terms of several parameters and for different inputs. The dynamics of the model was then compared to human perception investigated psychophysically during long presentations of an ambiguous, multistable motion pattern known as the barberpole illusion. In order to do this, the model is operated in a parameter range where known physiological response properties are reproduced whilst also working close to bifurcation. The model accounts for characteristic behaviour from the psychophysical experiments in terms of the type of switching observed and changes in the rate of switching with respect to contrast. In this way, the modelling study sheds light on the underlying mechanisms that drive perceptual switching in different contrast regimes. The general approach presented is applicable to a broad range of perceptual competition problems in which spatial interactions play a role

Cortical microcircuit dynamics in visual awareness

When a subject is dichoptically presented with two conflicting images, only one image is perceived at a time while the other is suppressed from awareness; this paradigm of multistable perception, is known as Binocular Rivalry (BR). Perception, therefore, alternates between the two visual patterns allowing a dissociation of sensory stimulation from conscious visual perception. From a theoretical point of view, most of the computational models proposed to account for BR are rate-like models. The need is nevertheless apparent to employ biophysically plausible neuronal network models in order to connect psychophysics experiments with neurophysiological data. Here we adopted a spiking network with biophysically realistic AMPA, NMDA, and GABA receptor-mediated synaptic dynamics, as well as spike-frequency adaptation mechanisms based on Ca++-activated K+ after-hyperpolarization currents. Noise due to the probabilistic spike times of neurons is crucial for rivalry. It has been shown that competition models based on cross-inhibition and adaptation explain the observed alternations in perception when noise operates in balance with adaptation. In order to gain insights into the cortical microcircuit dynamics mediating spontaneous perceptual alternations in BR, we derived a consistently reduced four-variable population rate model from a recurrent attractorbased biologically realistic spiking network used to model working memory, attention, and decision-making, where neuronal adaptation is implemented, using mean-field techniques. The model accounts for experimental data, collected from human subjects during BR, such as mean dominance duration, coefficient of variation, shape parameter of the gamma distribution of dominance durations and agrees with Levelt’s second and fourth proposition. The model replicates the observed data when it operates near the bifurcation that separates the noise-driven-transitions from the adaptation-driven-oscillations dynamical regime. Moreover, we show that spike-frequency adaptation of interneurons is not crucial for the spontaneous perceptual alternations, but affects the optimal parametric space of the system by decreasing the overall level of neuronal adaptation necessary for the bifurcation to occur and generates oscillations in resting state, such as in the absence of external stimuli. Furthermore, we consider recent experimental data from the macaque lateral prefrontal cortex collected during Binocular Flash Suppression a paradigm of externally induced perceptual alternation. They show a decrease in correlated variability across pairs of neurons sharing similar stimulus preferences when their preferred stimulus is perceived during rivalrous visual stimulation compared to the magnitude of correlation when the same stimulus is perceived without competition. Employing the biophysically plausible spiking network with spike-frequency adaptation, we explore distinct possible computational strategies responsible for the noise correlation decrease under visual competition