Bayesian reasoning is a machine learning approach that provides explainable outputs and excels in small-data situations with high uncertainty. However, it requires intensive memory access and computation and is, therefore, too energy-intensive for extreme edge contexts. Near-memory computation with memristors (or RRAM) can greatly improve the energy efficiency of its computations. Here, we report two fabricated integrated circuits in a hybrid CMOS-memristor process, featuring each sixteen tiny memristor arrays and the associated near-memory logic for Bayesian inference. One circuit performs Bayesian inference using stochastic computing, and the other uses logarithmic computation; these two paradigms fit the area constraints of near-memory computing well. On-chip measurements show the viability of both approaches with respect to memristor imperfections. The two Bayesian machines also operated well at low supply voltages. We also designed scaled-up versions of the machines. Both scaled-up designs can perform a gesture recognition task using orders of magnitude less energy than a microcontroller unit. We also see that if an accuracy lower than 86.9% is sufficient for this sample task, stochastic computing consumes less energy than logarithmic computing; for higher accuracies, logarithmic computation is more energy-efficient. These results highlight the potential of memristor-based near-memory Bayesian computing, providing both accuracy and energy efficiency.

The display of space filling data is still a challenge for the community of visualization. Direct volume rendering (DVR) is one of the most important techniques developed to achieve direct perception of such volumetric data. It is based on semitransparent representations, where the data are accumulated in a depth-dependent order. However, it produces images that may be difficult to understand, and thus several techniques have been proposed so as to improve its effectiveness, using for instance lighting models or simpler representations (e.g., maximum intensity projection). In this article, we present three perceptual studies that examine how DVR meets its goals, in either static or dynamic context. We show that a static representation is highly ambiguous, even in simple cases, but this can be counterbalanced by use of dynamic cues (i.e., motion parallax) provided that the rendering parameters are correctly tuned. In addition, perspective projections are demonstrated to provide relevant information to disambiguate depth perception in dynamic displays.

The execution of a simple pointing task invokes a chain of processing that includes visual acquisition of the target, coordination of multimodal proprioceptive signals, and ultimately the generation of a motor command that will drive the finger to the desired target location. These processes in the sensorimotor chain can be described in terms of internal representations of the target or limb positions and coordinate transformations between different internal reference frames. In this article we first describe how different types of error analysis can be used to identify properties of the internal representations and coordinate transformations within the central nervous system. We then describe a series of experiments in which subjects pointed to remembered 3D visual targets under two lighting conditions (dim light and total darkness) and after two different memory delays (0.5 and 5.0 s) and report results in terms of variable error, constant error, and local distortion. Finally, we present a set of simulations to help explain the patterns of errors produced in this pointing task. These analyses and experiments provide insight into the structure of the underlying sensorimotor processes employed by the central nervous system.