Neuromorphic Ai and Engineering

The guys at https://t.me/QuantumVault just uploaded the most recent science research articles on neuromorphic computing, engineering, ai, memristors and robotics on googles new Notes LLM.

"The first source explains the process of using ultra-thin AgBiS2 nanocrystal layers to stimulate neurons via near-infrared light. The second source explores the combinatorial design of textured mechanical metamaterials which are capable of shape morphing. The third source highlights the development of electrochemically reconfigurable architected materials which can be used to create tunable phononic crystals, beyond-intercalation battery electrodes, and bio-implantable devices. The fourth source examines the use of embodied neuromorphic intelligence in robotics by applying principles of spiking neural networks (SNNs) to sensory processing, motor control, and decision-making. The fifth source investigates the potential of memcapacitor devices for neuromorphic computing, which offers a highly energy-efficient approach for implementing parallel multiply-accumulate operations. The sixth source describes the experimental realization of on-chip topological nanoelectromechanical metamaterials (NEMM) which utilize free-standing silicon nitride (SiN) nanomembranes to achieve unidirectional waveguiding. The seventh source presents a flexible three-dimensional artificial synapse network (3D-ASN) based on selector-device-free electronic synapses (e-synapses) which exhibit properties like LTP/LTD, STDP learning, and PPF learning, along with ultralow-power consumption. The eighth source focuses on the development of four-dimensional (4D) direct laser writing (DLW) for the fabrication of reconfigurable compound micromachines. The ninth source explores the potential of generative complex networks within a dynamic memristor with intrinsic variability, showcasing the use of memristive devices for reservoir computing. The final source delves into the use of graph neural networks for metasurface modeling and examines the performance of the network based on parameters like connection radius and window size."