Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity, modeled by simple tuning curve functions. This has recently been demonstrated using Gaussian processes, with applications to realistic and topologically relevant latent manifolds. Those and previous models, however, missed crucial shared coding properties of neural populations. We propose feature sharing across neural tuning curves, which significantly improves performance and leads to better-behaved optimization. We also propose a solution to the problem of ensemble detection, whereby different groups of neurons, i.e., ensembles, can be modulated by different latent manifolds. This is achieved through a soft clustering of neurons during training, thus allowing for the separation of mixed neural populations in an unsupervised manner. These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds. Finally, we apply our method on a recently published grid cell dataset, recovering distinct ensembles, inferring toroidal latents and predicting neural tuning curves all in a single integrated modeling framework

Selection on Floral Morphology and Environmental Determinants of Fecundity in a Hawk Moth-Pollinated Violet

This paper presents the results of a 5—yr field study on the determinants of individual variation in maternal fecundity (seed production) in the narrowly endemic violet Viola cazorlensis (Violaceae), at a southeastern Spanish locality. Flowers of this species are characterized by a very long, thin spur and broad morphological variability, and are pollinated by a single species of day—flying hawk moth (Macroglossum stellatarum; Lepidoptera, Sphingidae). The primary aim of this investigation was to answer the question, What are the relative importances, as explanations of individual differences in fecundity, of variability in floral traits and of other fecundity determinants that are of an extrinsic nature, such as microhabitat type and interactions with herbivores? The floral morphology of individual V. cazorlensis plants was characterized by means of both "conventional," linear measurements of the size of flower parts (petals, spur, peduncle), and shape analysis of corolla outline (using thin—plate splines relative warps analysis). Spatial (among substrate types) and temporal (among years) patterns of variation in flower, fruit, and seed production by V. cazorlensis plants are described, with particular emphasis on the comparative effects of floral morphology, herbivory (by mammalian ungulates and two species of lepidopteran larvae), and substrate type (rock cliffs, bare rocks at ground level, and sandy soils), on cumulative seed production at the individual plant level. Cumulative seed production of individual V. cazorlensis plants depended significantly on average floral morphology (both size and shape components), thus revealing the existence of phenotypic selection on the floral morphology of this species at the study population. Among all the floral traits examined, spur length was the only one for which no significant relationship with fecundity was found. Type of substrate largely determined differences between V. cazorlensis plants in the impact of herbivory (plants growing on the soil exhibited the greatest reproductive losses to herbivores), and it also influenced plant size and flower production per reproductive episode. Plant size, in turn, influenced the supra—annual frequency of flowering and the number of flowers produced in each reproductive event. Flower production and herbivory levels significantly influenced (positively and negatively, respectively) fruit number, which was the major direct determinant of seed production. Path analysis revealed that the main determinants of individual variation in cumulative seed production over the study period were, in decreasing order of importance (absolute value of "effect coefficient" in parentheses), cumulative fruit production (0.946), mean flower production per reproductive event (0.868), plant size (0.441), herbivory by ungulates (—0.221), and average score on the first relative warp (0.107), a descriptor of flower shape. After accounting for the effects of substrate type, herbivory, plant size, and flower and fruit production, individual variation in floral morphology (aspects of size and shape) explained a negligible proportion (2.1%) of total individual variation in cumulative fruit production. Phenotypic selection on the floral morphology of V. cazorlensis at the study population, although statistically significant, was therefore almost inconsequential as a source of individual variation in maternal fitness, its effects being heavily "dilute" by the overwhelming influence of other factors. As exemplified by this study, selection on the floral phenotype may often become largely irrelevant in evolutionary terms because other ecological factors are far more important determinants of fitness differences among plants. A realistic assessment of the potential relevance of selection on plant reproductive traits thus requires a quantitative evaluation, in its natural scenario, of the predictable consequences of such selectionPeer reviewe

TRH: Pathophysiologic and clinical implications

Thyrotropin releasing hormone is thought to be a tonic stimulator of the pituitary TSH secretion regulating the setpoint of the thyrotrophs to the suppressive effect of thyroid hormones. The peptide stimulates the release of normal and elevated prolactin. ACTH and GH may increase in response to exogenous TRH in pituitary ACTH and GH hypersecretion syndromes and in some extrapituitary diseases. The pathophysiological implications of extrahypothalamic TRH in humans are essentially unknown. The TSH response to TRH is nowadays widely used as a diganostic amplifier in thyroid diseases being suppressed in borderline and overt hyperthyroid states and increased in primary thyroid failure. In hypothyroid states of hypothalamic origin, TSH increases in response to exogenous TRH often with a delayed and/or exaggerated time course. But in patients with pituitary tumors and suprasellar extension TSH may also respond to TRH despite secondary hypothyroidism. This TSH increase may indicate a suprasellar cause for the secondary hypothyroidism, probably due to portal vessel occlusion. The TSH released in these cases is shown to be biologically inactive

The multiwavelength properties of red QSOs: Evidence for dusty winds as the origin of QSO reddening

Fundamental differences in the radio properties of red quasars (QSOs), as compared to blue QSOs, have been recently discovered, positioning them as a potential key population in the evolution of galaxies and black holes across cosmic time. To elucidate the nature of these objects, we exploited a rich compilation of broad-band photometry and spectroscopic data to model their spectral energy distributions (SEDs) from the ultraviolet to the far-infrared and characterise their emission-line properties. Following a systematic comparison approach, we characterise the properties of the QSO accretion, obscuration, and host galaxies in a sample of ∼1800 QSOs at 0.2 z 1000 km s−1) in red QSOs as compared to the control sample. We find that red QSOs that exhibit evidence for high-velocity wind components present a stronger signature of the infrared excess, suggesting a causal connection between QSO reddening and the presence of hot dust distributions in QSO winds. We propose that dusty winds at nuclear scales are potentially the physical ingredient responsible for the optical colours in red QSOs, as well as a key parameter for the regulation of accretion material in the nucleus.</p

Neural system identification for large populations separating “what” and “where”

Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional neural system identification methods do not capitalize on this separation of “what” and “where”. Learning deep convolutional feature spaces shared among many neurons provides an exciting path forward, but the architectural design needs to account for data limitations: While new experimental techniques enable recordings from thousands of neurons, experimental time is limited so that one can sample only a small fraction of each neuron’s response space. Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural data is the estimation of the individual receptive field locations – a problem that has been scratched only at the surface thus far. We propose a CNN architecture with a sparse pooling layer factorizing the spatial (where) and feature (what) dimensions. Our network scales well to thousands of neurons and short recordings and can be trained end-to-end. We explore this architecture on ground-truth data to explore the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms current state-of-the art system identification models in the mouse visual system