Continual learning in neural networks addresses the challenge of adapting to new information accumulated over time while retaining previously acquired knowledge. A central obstacle to this process is ...

How does artificial intelligence continue to improve its capabilities? For a long time, expanding model size has been regarded as an important way to ...

Can AI learn by shrinking? A new study introduces a development-inspired continual learning framework for spiking neural ...

Can living neurons replace AI? A new study shows that biological neural networks (BNNs) can be trained to perform reservoir computing. Researcher used rat neurons to generate complex time-series ...

A human infant is born with roughly twice as many synapses as it will eventually need. Over the first few years of life, the ...

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

The advent of high-density recording technologies, such as Neuropixels and large-scale calcium imaging, has provided an unprecedented look into the ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

Artificial intelligence terminology continues to expand as researchers and companies develop new systems, prompting the need ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

Previously met with skepticism, AI won scientists a Nobel Prize for Chemistry in 2024 after they used it to solve the protein folding and design problem, and it has now been adopted by biologists ...