Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

Conveying data uncertainty in visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. This paper proposes a methodology for efficiently generating density plots of uncertain multivariate data sets that draws viewers to preattentively identify values of high certainty while not calling attention to uncertain values. We demonstrate how to augment scatter plots and parallel coordinates plots to incorporate statistically modeled uncertainty and show how to integrate them with existing multivariate analysis techniques, including outlier detection and interactive brushing. Computing high quality density plots can be expensive for large data sets, so we also describe a probabilistic plotting technique that summarizes the data without requiring explicit density plot computation. These techniques have been useful for identifying brain tumors in multivariate magnetic resonance spectroscopy data and we describe how to extend them to visualize ensemble data sets.

We present a semi-automatic framework for construction of curved and polygonal 3D models from a 2D line drawing, such as architectural or mechanical drawings. Despite advances in image-based modeling, 3D modeling from a drawn image remains largely manual. In contrast, our method only requires the user to annotate the source image with a drawn cube for camera calibration. 3D models are then generated automatically. The modeling process has four steps: camera calibration, a novel line detection algorithm for noisy input, line labeling to calculate polygon adjacencies, a new incremental construction method that uses plane hinging-angle optimization to improve scalability over previous approaches. We also present algorithms for handling curved surfaces when they are part of a polygonal model that provides boundary conditions.

Abstract We present a framework for interactive sketching that allows users to create three‐dimensional (3D) architectural models quickly and easily from a source drawing. The sketching process has four steps. (1) The user calibrates a viewing camera by specifying the origin and vanishing points of the drawing. (2) The user outlines surface polygons in the drawing. (3) A 3D reconstruction algorithm uses perceptual constraints to determine the closest visual fit for the polygon. (4) The user can then adjust aesthetic controls to produce several stylistic effects in the scene: a smooth transition between day and night rendering, a horizon knockout effect and entourage figures. The major advantage of our approach lies in the combination of perception‐based techniques, which allow us to minimize unnecessary interactions, and a hinging‐angle scheme, which shows significant improvement in numerical stability over previous optimization‐based 3D reconstruction algorithms. We also demonstrate how our reconstruction algorithm can be extended to work with perspective images, a feature unavailable in previous approaches.