Transformers are a neural network (NN) architecture, or model, that excels at processing sequential data by weighing the ...

Understanding how the brain learns and applies rules is the key to unraveling the neural basis of flexible behavior. A new ...

The Navier–Stokes partial differential equation was developed in the early 19th century by Claude-Louis Navier and George ...

The brain does not need its sophisticated cortex to interpret the visual world. A new study published in PLOS Biology ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

1 College of Electronic Science and Technology, National University of Defense Technology, Changsha, China 2 College of Electronics and Internet of Things, Chongqing Polytechnic University of ...

This is my journey to implement NNs from first principles, one neuron at a time. In this notebook we build a neural network with 2 neurons in layer 1, and 1 neuron in layer 2. We then visualize how it ...

The first howl of a dire wolf in 10,000 years moved Peter Jackson to tears, but for scientists, these vocalizations represent something even more profound: direct evidence that genetic engineering can ...

The brain criticality hypothesis has been a central research topic in theoretical neuroscience for two decades. This hypothesis suggests that the brain operates near the critical point at the boundary ...

In the rapidly evolving artificial intelligence landscape, one of the most persistent challenges has been the resource-intensive process of optimizing neural networks for deployment. While AI tools ...

Code Repository for the paper: "Structure Is Not Enough: Leveraging Behavior for Neural Network Weight Reconstruction" at the ICLR 2025 Workshop on Neural Network Weights as a New Data Modality (WSL ...