Computational model links normalization to chemoarchitecture in the human visual system.

A goal of cognitive neuroscience is to provide computational accounts of brain function. Canonical computations-mathematical operations used by the brain in many contexts-fulfill broad information-processing needs by varying their algorithmic parameters. A key question concerns the identification of biological substrates for these computations and their algorithms.

More than meets the eye: The role of sensory dimensions in psychedelic brain dynamics, experience, and therapeutics.

Psychedelics are undergoing a major resurgence of scientific and clinical interest. While multiple theories and frameworks have been proposed, there is yet no universal agreement on the mechanisms underlying the complex effects of psychedelics on subjective experience and brain dynamics, nor their therapeutic benefits. Despite being prominent in psychedelic phenomenology and distinct from those elicited by other classes of hallucinogens, the effects of psychedelics on low-level sensory - particularly visual - dimensions of experience, and corresponding brain dynamics, have often been disregarded by contemporary research as 'epiphenomenal byproducts'.

Correction: Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome.

[This corrects the article DOI: 10.1371/journal.pcbi.

Divisive normalization unifies disparate response signatures throughout the human visual hierarchy.

Neural processing is hypothesized to apply the same mathematical operations in a variety of contexts, implementing so-called canonical neural computations. Divisive normalization (DN) is considered a prime candidate for a canonical computation. Here, we propose a population receptive field (pRF) model based on DN and evaluate it using ultra-high-field functional MRI (fMRI).

Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome.

Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain.

Motility of active nematic films driven by "active anchoring".

We provide a minimal model for an active nematic film in contact with both a solid substrate and a passive isotropic fluid, and explore its dynamics in one and two dimensions using a combination of hybrid Lattice Boltzmann simulations and analytical calculations. By imposing nematic anchoring at the substrate while active flows induce a preferred alignment at the interface ("active anchoring"), we demonstrate that directed fluid flow spontaneously emerges in cases where the two anchoring types are opposing. In one dimension, our model reduces to an analogue of a loaded elastic column.