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The amyloid precursor protein (APP) is linked to the genetics and pathogenesis of Alzheimer's disease (AD). It is the parent protein of the β-amyloid (Aβ) peptide, the main constituent of the amyloid plaques found in an AD brain. The pathways from APP to Aβ are intensively studied, yet the normal functions of APP itself have generated less interest. We report here that glutamate stimulation of neuronal activity leads to a rapid increase inAppgene expression. In mouse and human neurons, elevated APP protein changes the structure of the axon initial segment (AIS) where action potentials are initiated. The AIS is shortened in length and shifts away from the cell body. The GCaMP8f Ca²⁺reporter confirms the predicted decrease in neuronal activity. NMDA antagonists or knockdown ofAppblock the glutamate effects. The actions of APP on the AIS are cell-autonomous; exogenous Aβ, either fibrillar or oligomeric, has no effect. In culture, APP_Swe(a familial AD mutation) induces larger AIS changes than wild type APP. Ankyrin G and βIV-spectrin, scaffolding proteins of the AIS, both physically associate with APP, more so in AD brains. Finally, in humans with sporadic AD or in the R1.40 AD mouse model, both females and males, neurons have elevated levels of APP protein that invade the AIS.In vivoasin vitro, this increased APP is associated with a significant shortening of the AIS. The findings outline a new role for the APP and encourage a reconsideration of its relationship to AD. SIGNIFICANCE STATEMENTWhile the amyloid precursor protein (APP) has long been associated with Alzheimer's disease (AD), the normal functions of the full-length Type I membrane protein have been largely unexplored. We report here that the levels of APP protein increase with neuronal activity.In vivoandin vitro, modest amounts of excess APP alter the properties of the axon initial segment. The β-amyloid peptide derived from APP is without effect. Consistent with the observed changes in the axon initial segment which would be expected to decrease action potential firing, we show that APP expression depresses neuronal activity. In mouse AD models and human sporadic AD, APP physically associates with the scaffolding proteins of the axon initial segment, suggesting a relationship with AD dementia. 

Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control. 

Neuromorphic computing has the great potential to enable faster and more energy‐efficient computing by overcoming the von Neumann bottleneck. However, most emerging nonvolatile memory (NVM)‐based artificial synapses suffer from insufficient precision, nonlinear synaptic weight update, high write voltage, and high switching latency. Moreover, the spatiotemporal dynamics, an important temporal component for cognitive computing in spiking neural networks (SNNs), are hard to generate with existing complementary metal–oxide–semiconductor (CMOS) devices or emerging NVM. Herein, a three‐terminal, Li_xWO₃‐based electrochemical synapse (LiWES) is developed with low programming voltage (0.2 V), fast programming speed (500 ns), and high precision (1024 states) that is ideal for artificial neural networks applications. Time‐dependent synaptic functions such as paired‐pulse facilitation (PPF) and temporal filtering that are critical for SNNs are also demonstrated. In addition, by leveraging the spike‐encoded timing information extracted from the short‐term plasticity (STP) behavior in the LiWES, an SNNs model is built to benchmark the pattern classification performance of the LiWES, and the result indicates a large boost in classification performance (up to 128×), compared with those NO‐STP synapses.