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A new class of neuromorphic processors promises to provide fast and power-efficient execution of spiking neural networks with on-chip synaptic plasticity. This efficiency derives in part from the fine-grained parallelism as well as event-driven communication mediated by spatially and temporally sparse spike messages. Another source of efficiency arises from the close spatial proximity between synapses and the sites where their weights are applied and updated. This proximity of compute and memory elements drastically reduces expensive data movements but imposes the constraint that only local operations can be efficiently performed, similar to constraints present in biological neural circuits. Efficient weight update operations should therefore only depend on information available locally at each synapse as non-local operations that involve copying, taking a transpose, or normalizing an entire weight matrix are not efficiently supported by present neuromorphic architectures. Moreover, spikes are typically non-negative events, which imposes additional constraints on how local weight update operations can be performed. The Locally Competitive Algorithm (LCA) is a dynamical sparse solver that uses only local computations between non-spiking leaky integrator neurons, allowing for massively parallel implementations on compatible neuromorphic architectures such as Intel's Loihi research chip. It has been previously demonstrated that non-spiking LCA can be used to learn dictionaries of convolutional kernels in an unsupervised manner from raw, unlabeled input, although only by employing non-local computation and signed non-spiking outputs. Here, we show how unsupervised dictionary learning with spiking LCA (S-LCA) can be implemented using only local computation and unsigned spike events, providing a promising strategy for constructing self-organizing neuromorphic chips.more » « less
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Abstract Impact angle plays a significant role in determining the fate of the projectile. In this study, we use a suite of hypervelocity impact experiments to reveal how impact angle affects the preservation, distribution, and physical state of projectile residues in impact craters. Diverse types of projectiles, including amorphous silicates, crystalline silicates, and aluminum, in two sizes (6.35 and 12.7 mm), were launched into blocks of copper or 6061 aluminum at speeds between 1.9 and 5.7 km s−1. Crater interiors preserve projectile residues in all cases, including conditions relevant to the asteroid belt. These residues consist of projectile fragments or projectile‐rich glasses, depending on impact conditions. During oblique impacts at 30° and 45°, the uprange crater wall preserves crystalline fragments of the projectile. The fragments of water‐rich projectiles such as antigorite remain hydrated. Several factors contribute to enhanced preservation on the uprange wall, including a weaker shock uprange, uprange acceleration as the shock reflects off the back of the projectile, and rapid quenching of melts along the projectile–target interface. These findings have two broader implications. First, the results suggest a new collection strategy for flyby sample return missions. Second, these results predict that the M‐type asteroid Psyche should bear exogenic, impactor‐derived debris.