Can a neural network be constructed entirely from DNA and yet learn in the same way as its silicon-based brethren? Recent ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech ...

Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...

First, the ILC outputs of multiple tasks are compressed into a function by the proposed method, and thus, the memories can be saved. Second, in terms of generalizability, the neural-network-based ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher ...

The performance of the proposed sensor demonstrates that the aliasing spectral decoupling algorithm based on neural network combined with wavelength-modulated spectroscopy technology has the ...

This project involves implementing the forward pass of an 18-layer Convolutional Neural Network (CNN) in MATLAB for object detection. The goal is to classify 32x32x3 images into one of ten categories, ...

This brief discusses the output synchronization problem for coupled neural networks with multiple delayed output couplings (CNNMDOCs). On one hand, by utilizing adaptive state feedback controller and ...