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Deep neural networks (DNNs) optimized for visual tasks learn representations that align layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this finding is that hierarchical representations are necessary to accurately predict brain activity in the primate visual system. To test this interpretation, we optimized DNNs to directly predict brain activity measured with fMRI in human visual areas V1-V4. We trained a single-branch DNN to predict activity in all four visual areas jointly, and a multi-branch DNN to predict each visual area independently. Although it was possible for the multi-branch DNN to learn hierarchical representations, only the single-branch DNN did so. This result shows that hierarchical representations are not necessary to accurately predict human brain activity in V1-V4, and that DNNs that encode brain-like visual representations may differ widely in their architecture, ranging from strict serial hierarchies to multiple independent branches.

 

Converging, cross-species evidence indicates that memory for time is supported by hippocampal area CA1 and entorhinal cortex. However, limited evidence characterizes how these regions preserve temporal memories over long timescales (e.g., months). At long timescales, memoranda may be encountered in multiple temporal contexts, potentially creating interference. Here, using 7T fMRI, we measured CA1 and entorhinal activity patterns as human participants viewed thousands of natural scene images distributed, and repeated, across many months. We show that memory for an image’s original temporal context was predicted by the degree to which CA1/entorhinal activity patterns from the first encounter with an image were re-expressed during re-encounters occurring minutes to months later. Critically, temporal memory signals were dissociable from predictors of recognition confidence, which were carried by distinct medial temporal lobe expressions. These findings suggest that CA1 and entorhinal cortex preserve temporal memories across long timescales by coding for and reinstating temporal context information.

 

The brain mechanisms of memory consolidation remain elusive. Here, we examine blood-oxygen-level-dependent (BOLD) correlates of image recognition through the scope of multiple influential systems consolidation theories. We utilize the longitudinal Natural Scenes Dataset, a 7-Tesla functional magnetic resonance imaging human study in which ∼135,000 trials of image recognition were conducted over the span of a year among eight subjects. We find that early- and late-stage image recognition associates with both medial temporal lobe (MTL) and visual cortex when evaluating regional activations and a multivariate classifier. Supporting multiple-trace theory (MTT), parts of the MTL activation time course show remarkable fit to a 20-y-old MTT time-dynamical model predicting early trace intensity increases and slight subsequent interference ( R 2 > 0.90). These findings contrast a simplistic, yet common, view that memory traces are transferred from MTL to cortex. Next, we test the hypothesis that the MTL trace signature of memory consolidation should also reflect synaptic “desaturation,” as evidenced by an increased signal-to-noise ratio. We find that the magnitude of relative BOLD enhancement among surviving memories is positively linked to the rate of removal (i.e., forgetting) of competing traces. Moreover, an image-feature and time interaction of MTL and visual cortex functional connectivity suggests that consolidation mechanisms improve the specificity of a distributed trace. These neurobiological effects do not replicate on a shorter timescale (within a session), implicating a prolonged, offline process. While recognition can potentially involve cognitive processes outside of memory retrieval (e.g., re-encoding), our work largely favors MTT and desaturation as perhaps complementary consolidative memory mechanisms. 

This paper presents the design of a new soft pneumatic actuator whose direction and magnitude of bending may be precisely controlled via activation of different shape memory alloy (SMA) springs within the actuator, in conjunction with pneumatic actuation. This design is inspired by examples seen in nature such as the human tongue, where the combination of hydrostatic pressure and contraction of intrinsic muscle groups enables precise maneuverability and morphing capabilities. Here, SMA springs are embedded in the walls of the actuator, serving as intrinsic muscles that may be selectively activated to constrain the device. The pneumatic SMA (PneuSMA) actuator demonstrates remarkable spatial controllability evidenced by testing under different pressures and SMA activation combinations. A baseline finite element model is also developed to predict the actuator deformation under different pressure and activation conditions. 

Variable stiffness structures lie at the nexus of soft robots and traditional robots as they enable the execution of both high-force tasks and delicate manipulations. Laminar jamming structures, which consist of thin flexible sheets encased in a sealed chamber, can alternate between a rigid state when a vacuum is applied and a flexible state when the layers are allowed to slide in the absence of a pressure gradient. In this work, an additional mode of controllability is added by clamping and unclamping the ends of a simple laminar jamming beam structure. Previous works have focused on the translational degree of freedom that may be controlled via vacuum pressure; here we introduce a rotational degree of freedom that may be independently controlled with a clamping mechanism. Preliminary results demonstrate the ability to switch between three states: high stiffness (under vacuum), translational freedom (with clamped ends, no vacuum), and rotational freedom (with ends free to slide, no vacuum).

 

Computer simulations for physics labs may be combined with hands-on lab equipment to boost student understanding and make labs more accessible. Hybrid labs of HTML5-based computer simulations and hands-on lab equipment for topics in mechanics were investigated in a large, algebra-based, studio physics course for life science students at a private, research-intensive institution. Computer simulations were combined with hands-on equipment and compared to traditional hands-on labs using an A/B testing protocol. Learning outcomes were measured for the specific topic of momentum conservation by comparing student scores on post-lab exercises, related quiz and exam questions, and a subset of questions on the Energy and Momentum Conceptual Survey (EMCS) administered before and after instruction for both groups. We find that students who completed a hands-on lab vs. a hybrid lab showed no difference in performance on momentum assessments.