Rats exhibit significant recovery of locomotor function following incomplete spinal cord injuries, albeit with altered gait expression and reduced speed and stepping frequency. These changes likely ...

Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like telling a leaf apart from a rock. But they have struggled to build ...

Spiking neural networks (SNNs), which are the next generation of artificial neural networks (ANNs), offer a closer mimicry to natural neural networks and hold promise for significant improvements in ...

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from ...

From the smallest fragment of brain tissue, the intricate blueprint of the entire brain is beginning to emerge. Researchers at Baylor College of Medicine are making several time-consuming aspects of ...

Because connectomic wiring diagrams are inherently incomplete, owing to errors in machine-learning-based neuron reconstructions, they require extensive human editing, which is a formidable ...

It's possible to get pretty much any neural network to run as a spiking neural network, but there are some significant challenges, like converting the input into a series of spikes in the first place.

Inspired by microscopic worms, Liquid AI’s founders developed a more adaptive, less energy-hungry kind of neural network. Now the MIT spin-off is revealing several new ultraefficient models.

By Dr. Chinta Sidharthan Although neurobiological studies have long studied the various regions of the brain, there was an absence of information on the interconnectedness between the regions and the ...

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher. Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost ...

A neural network is a series of algorithms that seek to identify relationships in a data set via a process that mimics how the human brain works.