spiking neural networks (SNN) continue to demonstrate great potential in the landscape of artificial intelligence, particularly in tasks such as recognition, inference, and learning. While SNN focuses ...

triggers the memory of the entire pattern (the song), without you actually having to hear the rest of the song again. We use this handy neural mechanism to learn, remember, solve problems and ...

and reveals a novel neural-glia-fibroblast-lymphatic regulatory axis. This provides a new framework for understanding how the brain adapts its lymphatic network based on functional needs ...

Abstract: Event camera-based pattern recognition is a newly arising research topic in recent years. Current researchers usually transform the event streams into images, graphs, or voxels, and adopt ...

to synthesize a deep neural network (DNN) alternative for pattern classification. Contrary to the DNNs trained from end to end by backpropagation (BP), each S-DNN layer, that is, a self-learnable ...

damage neural tissue in order to create this interface between brains and electrodes. Tim Higgins: And these are little hairs that go into the brain. Michael Mager: The original version of this is ...

This teaching package contains modular contents for the introduction of the fundamentals of Neural Networks. The package consists ... You will learn to use deep learning techniques in MATLAB for image ...

Convolutional Neural Networks (ConvNets or CNNs) are a class of neural networks algorithms that are mostly used in visual recognition tasks such as image classification, object detection, and image ...

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...

Find more information on the Altmetric Attention Score and how the score is calculated. Network structures of metal nanowires are a promising candidate for producing a wide range of flexible ...