Single neurons in mouse sensorimotor cortex are organized by their activity features into distinct subpopulations with area-spanning footprints whose boundaries align closely with anatomical and ...

As AI processing demands reach the limits of current CMOS technology, neuromorphic computing—hardware and software that mimic the human brain's structure—can help process information faster and more ...

Fringe movements are using games and other online platforms to draw growing numbers of children to their causes, new data and dozens of interviews show. By Pranav Baskar Taking a page from the child ...

Brain-computer interfaces (BCIs) leverage EEG signal processing to enable human-machine communication and have broad application potential. However, existing deep learning-based BCI methods face two ...

This is the MATLAB code for the implementation of neural pupil engineering FPM (NePE-FPM), an optimization framework for FPM reconstruction for off-axis areas. NePE-FPM engineers the pupil function ...

A distinguishing feature of the neural network models used in Physics and Chemistry is that they must obey basic underlying symmetries, such as symmetry to translations, rotations, and the exchange of ...

Abstract: In this paper, the hyperbolic tangent function and its application in a test neural network on a Field Programmable Gate Array are presented, using the Verilog hardware description language.

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In chemical reaction network theory, ordinary differential equations are used to model ...

Penn Engineers have developed the first programmable chip that can train nonlinear neural networks using light—a breakthrough that could dramatically speed up AI training, reduce energy use and even ...

Electroencephalography (EEG) is widely used for analyzing brain activity; however, the nonlinear and nature of EEG signals presents significant challenges for traditional analysis methods. Machine has ...