Abstract: Convolutional neural networks have remarkable performance in artificial intelligence, although at the cost of computationally demanding processes within a single inference. Simultaneously, ...

Today’s best robotics courses offer hands-on experience with circuits, ROS, Lidar, and more. Robots are everywhere, from the assembly line of major factories to the vacuums cleaning the living room ...

The spiking convolutional neural network (SCNN) is a kind of spiking neural network (SNN) with high accuracy for visual tasks and power efficiency on neuromorphic hardware, which is attractive for ...

Abstract: Field-programmable gate array (FPGA) has become an excellent hardware accelerator solution for convolutional neural networks (CNNs). Meanwhile, optimizing methods, such as model compression, ...

Convolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their ...

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires ...

Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, ...

Simple, high-resolution methods for visualizing complex neural circuitry in 3D in the intact mammalian brain are revolutionizing the way researchers study brain connectivity and function. However, ...

Cues predictive of target locations orient covert attention, improving perceptual performance. Studies have focused on attentional influences on neural activity, but how cues activate attention and ...