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. Chemoarchitecture-the spatial distribution of neurotransmitter receptor densities-shapes brain function. Here, we propose that local variations in specific receptor densities implement algorithmic modulations of canonical computations. To test this hypothesis, we combine mathematical modeling of brain responses with chemoarchitecture data. We compare parameters of divisive normalization obtained from 7-tesla functional magnetic resonance imaging with receptor density maps obtained from positron emission tomography. We find evidence that serotonin and γ-aminobutyric acid receptor densities are the biological substrate for algorithmic modulations of divisive normalization in the human visual system. Our model links computational and biological levels of vision, explaining how canonical computations allow the brain to fulfill broad information-processing needs.

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'. Here, we review available evidence from neuroimaging, pharmacology, questionnaires, and clinical studies; we propose extensions to existing models, provide testable hypotheses for the potential therapeutic roles of psychedelic-induced visual hallucinations, and simulations of visual phenomena relying on low-level cortical dynamics. In sum, we show that psychedelic-induced alterations in low-level sensory dimensions 1) are unlikely to be entirely causally reconducible to high-level alterations, but rather co-occur with them in a dialogical interplay, and 2) are likely to play a causally relevant role in determining high-level alterations and therapeutic outcomes. We conclude that reevaluating the currently underappreciated role of sensory dimensions in psychedelic states will be highly valuable for neuroscience and clinical practice, and that integrating low-level and domain-specific aspects of psychedelic effects into existing nonspecific models is a necessary step to further understand how these substances effect both acute and long-term change in the human brain.

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

[This corrects the article DOI: 10.1371/journal.pcbi.1008310.].

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). The DN model parsimoniously captures seemingly disparate response signatures with a single computation, superseding existing pRF models in both performance and biological plausibility. We observe systematic variations in specific DN model parameters across the visual hierarchy and show how they relate to differences in response modulation and visuospatial information integration. The DN model delivers a unifying framework for visuospatial responses throughout the human visual hierarchy and provides insights into its underlying information-encoding computations. These findings extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy.

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. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.

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. Here, the transition from a stationary to a motile state is akin to the buckling bifurcation, but offers the possibility to reverse the flow direction for a given set of parameters and boundary conditions solely by changing initial conditions. The two-dimensional variant of our model allows for additional tangential instabilities, and it is found that undulations form in the interface above a threshold activity. Our results might be relevant for the design of active microfluidic geometries or curvature-guided self-assembly.