The relation of phase noise and luminance contrast to overt attention in complex visual stimuli

Models of attention are typically based on difference maps in low-level features but neglect higher order stimulus structure. To what extent does higher order statistics affect human attention in natural stimuli? We recorded eye movements while observers viewed unmodified and modified images of natural scenes. Modifications included contrast modulations (resulting in changes to first- and second-order statistics), as well as the addition of noise to the Fourier phase (resulting in changes to higher order statistics). We have the following findings: (1) Subjects' interpretation of a stimulus as a “natural” depiction of an outdoor scene depends on higher order statistics in a highly nonlinear, categorical fashion. (2) Confirming previous findings, contrast is elevated at fixated locations for a variety of stimulus categories. In addition, we find that the size of this elevation depends on higher order statistics and reduces with increasing phase noise. (3) Global modulations of contrast bias eye position toward high contrasts, consistent with a linear effect of contrast on fixation probability. This bias is independent of phase noise. (4) Small patches of locally decreased contrast repel eye position less than large patches of the same aggregate area, irrespective of phase noise. Our findings provide evidence that deviations from surrounding statistics, rather than contrast per se, underlie the well-established relation of contrast to fixation

Gunnera herteri - developmental morphology of a dwarf from Uruguay and S Brazil (Gunneraceae)

Abstract.: New morphological and developmental observations are presented of Gunnera herteri (subgenus Ostenigunnera) which is, according to molecular studies, sister to the other species of Gunnera. It is an annual dwarf (up to 4 cm long) whereas the other Gunnera spp. are perennial and slightly to extremely larger. External stem glands are combined with channels into the stem cortex serving as entrance path for symbiotic Nostoc cells. Young stem zones show globular regions of cytoplasm-rich cortex cells, prepared for invasion by Nostoc. The leaf axils contain 2-5 inconspicuous colleters (glandular scales) which can be taken as homologous to the more prominent scales of G. manicata (subg. Panke) and G. macrophylla (subg. Pseudogunnera). Foliage leaves of G. herteri have tooth-like sheath lobes which may be homologous to stipules. Adult plants have extra-axillary inflorescences arising from leaf nodes. The main stem is interpreted as a chain of sympodial units, each one consisting of a leaf and an extra-axillary inflorescence. This "sympodium hypothesis” may be also valid for other species of Gunnera. Each globular inflorescence of G. herteri contains several female flowers and 2-7 stamens at the top, perhaps equalling a single male flower. There are neither bracts nor bracteoles. The ovary is inferior, bicarpellary and unilocular. Its single hanging ovule develops into a dry and endosperm-rich see

Computation in Dynamically Bounded Asymmetric Systems

Previous explanations of computations performed by recurrent networks have focused on symmetrically connected saturating neurons and their convergence toward attractors. Here we analyze the behavior of asymmetrical connected networks of linear threshold neurons, whose positive response is unbounded. We show that, for a wide range of parameters, this asymmetry brings interesting and computationally useful dynamical properties. When driven by input, the network explores potential solutions through highly unstable ‘expansion’ dynamics. This expansion is steered and constrained by negative divergence of the dynamics, which ensures that the dimensionality of the solution space continues to reduce until an acceptable solution manifold is reached. Then the system contracts stably on this manifold towards its final solution trajectory. The unstable positive feedback and cross inhibition that underlie expansion and divergence are common motifs in molecular and neuronal networks. Therefore we propose that very simple organizational constraints that combine these motifs can lead to spontaneous computation and so to the spontaneous modification of entropy that is characteristic of living systems

State-Dependent Computation Using Coupled Recurrent Networks

Although conditional branching between possible behavioral states is a hallmark of intelligent behavior, very little is known about the neuronal mechanisms that support this processing. In a step toward solving this problem, we demonstrate by theoretical analysis and simulation how networks of richly interconnected neurons, such as those observed in the superficial layers of the neocortex, can embed reliable, robust finite state machines. We show how a multistable neuronal network containing a number of states can be created very simply by coupling two recurrent networks whose synaptic weights have been configured for soft winner-take-all (sWTA) performance. These two sWTAs have simple, homogeneous, locally recurrent connectivity except for a small fraction of recurrent cross-connections between them, which are used to embed the required states. This coupling between the maps allows the network to continue to express the current state even after the input that elicited that state iswithdrawn. In addition, a small number of transition neurons implement the necessary input-driven transitions between the embedded states. We provide simple rules to systematically design and construct neuronal state machines of this kind. The significance of our finding is that it offers a method whereby the cortex could construct networks supporting a broad range of sophisticated processing by applying only small specializations to the same generic neuronal circuit

Investigating the interaction of cellulose nanofibers derived from cotton with a sophisticated 3D human lung cell coculture

Cellulose nanofibers are an attractive component of a broad range of nanomaterials. Their intriguing mechanical properties and low cost, as well as the renewable nature of cellulose make them an appealing alternative to carbon nanotubes (CNTs), which may pose a considerable health risk when inhaled. Little is known, however, concerning the potential toxicity of aerosolized cellulose nanofibers. Using a 3D in vitro triple cell coculture model of the human epithelial airway barrier, it was observed that cellulose nanofibers isolated from cotton (CCN) elicited a significantly (p < 0.05) lower cytotoxicity and (pro-)inflammatory response than multiwalled CNTs (MWCNTs) and crocidolite asbestos fibers (CAFs). Electron tomography analysis also revealed that the intracellular localization of CCNs is different from that of both MWCNTs and CAFs, indicating fundamental differences between each different nanofibre type in their interaction with the human lung cell coculture. Thus, the data shown in the present study highlights that not only the length and stiffness determine the potential detrimental (biological) effects of any nanofiber, but that the material used can significantly affect nanofiber–cell interactions

Risk assessment of released cellulose nanocrystals – mimicking inhalatory exposure

Cellulose nanocrystals (CNCs) exhibit advantageous chemical and mechanical properties that render them attractive for a wide range of applications. During the life-cycle of CNC containing materials the nanocrystals could be released and become airborne, posing a potential inhalatory exposure risk towards humans. Absent reliable and dose-controlled models that mimic this exposure in situ is a central issue in gaining an insight into the CNC-lung interaction. Here, an Air Liquid Interface Cell Exposure system (ALICE), previously designed for studies of spherical nanoparticles, was used for the first time to establish a realistic physiological exposure test method for inhaled fiber shaped nano-objects; in this case, CNCs isolated from cotton. Applying a microscopy based approach the spatially homogenous deposition of CNCs was demonstrated as a prerequisite of the functioning of the ALICE. Furthermore, reliability and controllability of the system to nebulise high aspect ratio nanomaterials (HARN, e.g. CNCs) was shown. This opens the potential to thoroughly investigate the inhalatory risk of CNCs in vitro using a realistic exposure system

Can timber provision from Amazonian production forests be sustainable?

Around 30 Mm3 of sawlogs are extracted annually by selective logging of natural production forests in Amazonia, Earth's most extensive tropical forest. Decisions concerning the management of these production forests will be of major importance for Amazonian forests' fate. To date, no regional assessment of selective logging sustainability supports decision-making. Based on data from 3500 ha of forest inventory plots, our modelling results show that the average periodic harvests of 20 m3 ha−1 will not recover by the end of a standard 30 year cutting cycle. Timber recovery within a cutting cycle is enhanced by commercial acceptance of more species and with the adoption of longer cutting cycles and lower logging intensities. Recovery rates are faster in Western Amazonia than on the Guiana Shield. Our simulations suggest that regardless of cutting cycle duration and logging intensities, selectively logged forests are unlikely to meet timber demands over the long term as timber stocks are predicted to steadily decline. There is thus an urgent need to develop an integrated forest resource management policy that combines active management of production forests with the restoration of degraded and secondary forests for timber production. Without better management, reduced timber harvests and continued timber production declines are unavoidable

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

Calcium-dependent dynamics of cadherin interactions at cell–cell junctions

Cadherins play a key role in the dynamics of cell–cell contact formation and remodeling of junctions and tissues. Cadherin–cadherin interactions are gated by extracellular Ca^(2+), which serves to rigidify the cadherin extracellular domains and promote trans junctional interactions. Here we describe the direct visualization and quantification of spatiotemporal dynamics of N-cadherin interactions across intercellular junctions in living cells using a genetically encodable FRET reporter system. Direct measurements of transjunctional cadherin interactions revealed a sudden, but partial, loss of homophilic interactions (τ = 1.17 ± 0.06 s^(−1)) upon chelation of extracellular Ca^(2+). A cadherin mutant with reduced adhesive activity (W2A) exhibited a faster, more substantial loss of homophilic interactions (τ = 0.86 ± 0.02 s^(−1)), suggesting two types of native cadherin interactions—one that is rapidly modulated by changes in extracellular Ca^(2+) and another with relatively stable adhesive activity that is Ca^(2+) independent. The Ca^(2+)-sensitive dynamics of cadherin interactions were transmitted to the cell interior where β-catenin translocated to N-cadherin at the junction in both cells. These data indicate that cadherins can rapidly convey dynamic information about the extracellular environment to both cells that comprise a junction