This work will be of interest to the motor control community as well as neuroAI researchers interested in how bodies constrain neural circuit function. The authors present "MotorNet", a useful ...

Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of-the-art ...

Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077 Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong ...

Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As ...

Public databases are an important resource for machine learning research, but their growing availability sometimes leads to “off-label” usage, where data published for one task are used for another.

The source codes for certain products of major Vietnamese cybersecurity firm Bkav are being offered on sale on a data leak forum for a total of $250,000. The leaks were recently posted on Raidforums, ...

How can we train spiking neural networks to achieve brain-like performance in machine learning tasks? The resounding success and pervasive use of the backpropagation algorithm in deep learning ...

1 Department of Electronics, Computing and Mathematics, University of Derby, Derby, UK. 2 Department of Computer Science and Intelligent Systems, Iwate University, Morioka, Japan. 3 BAC International ...