Nature

Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for ...
American Institute of Physics

Hybrid convolutional neural network yields energy-efficient approach with analog first layer

Oscillatory retinal neuron networks don’t require external voltage sources and show comparable performance to cutting-edge GPU-based convolutional neural networks, for energy costs thousands of times ...
InfoWorld

The most popular neural network styles and how they work

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...
Nature

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis

The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women’s health worldwide. However, the efficacy and cost ...
Geeky Gadgets

What is an AI neural network and how do they work?

AI neural networks, inspired by the human brain are a foundational technology in the field of artificial intelligence. They are designed to recognize patterns and make decisions with a level of ...
Bow Doin Polar Bears

Mingi Kang ’26: Advancing Computers' Ability to See and Understand Our World

Mingi Kang ’26 received a Fall Research Award from Bowdoin this semester to support his project exploring how two distinct concepts for computer vision might be combined to create a more robust ...