This paper concerns the use of adaptive wave cancellation in a new multilayer smart skin sensor to attenuate the primary low-frequency noise underwater. The proposed multilayered system is designed with a piezoelectric actuator (Pb(In1/2Nb1/2)O3–Pb(Mg1/3Nb2/3)O3–PbTiO3 composite) and two layers of polyvinylidene fluoride to accelerate wave absorption. Furthermore, the use of a combination of an adaptive control scheme and a time-delay signal separation method has the potential to provide the proposed absorber system with a wave cancellation capability and thereby enable the absorber system to respond to environmental changes underwater. The use of smart piezoelectric materials and an adaptive control approach enables the absorber system to achieve the high attenuation level of the reflected waves, unlike typical absorber systems based on active noise control. Echo reduction experiments showed that the proposed piezoelectric-based multilayer sensor with an adaptive controller could attenuate reflected wave signals effectively.
A key feature of many cortical systems is functional organization: the arrangement of neurons with specific functional properties in characteristic spatial patterns across the cortical surface. However, the principles underlying the emergence and utility of functional organization are poorly understood. Here we develop the Topographic Deep Artificial Neural Network (TDANN), the first unified model to accurately predict the functional organization of multiple cortical areas in the primate visual system. We analyze the key factors responsible for the TDANN's success and find that it strikes a balance between two specific objectives: achieving a task-general sensory representation that is self-supervised, and maximizing the smoothness of responses across the cortical sheet according to a metric that scales relative to cortical surface area. In turn, the representations learned by the TDANN are lower dimensional and more brain-like than those in models that lack a spatial smoothness constraint. Finally, we provide evidence that the TDANN's functional organization balances performance with inter-area connection length, and use the resulting models for a proof-of-principle optimization of cortical prosthetic design. Our results thus offer a unified principle for understanding functional organization and a novel view of the functional role of the visual system in particular.
Deep convolutional neural networks are biologically driven models that resemble the hierarchical structure of primate visual cortex and are the current best predictors of the neural responses measured along the ventral stream. However, the networks lack topographic properties that are present in the visual cortex, such as orientation maps in primary visual cortex and categoryselective maps in inferior temporal (IT) cortex. In this work, the minimum wiring cost constraint was approximated as an additional learning rule in order to generate topographic maps of the networks. We found that our topographic deep artificial neural networks (ANNs) can reproduce the category selectivity maps of the primate IT cortex.
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