Cognitive computational neuroscience has entered a transformative era. The rapid rise of large multimodal foundation models, state-space architectures, and ...

Abstract: With the accumulation of resources in the era of big data and the rise of pre-trained models in deep learning, optimizing neural networks for various tasks often involves different ...

A minimal neural network implementation for training and inference, built from scratch with NumPy. On a MacBook Air M2, one training epoch takes approximately 20 seconds. PyTorch is used only for its ...

NEURON has been widely used as an empirically-based simulation tool, especially for multi-compartment conductance-based neuronal modeling. The network mediating feeding in Aplysia californica has been ...

Forward-looking: Nvidia's latest push into neural rendering is not just unfolding on keynote stages, but also in follow-up technical briefings. A recent video released days after the DLSS 5 ...

We explore one possibility for relieving the U.S. housing crisis. By Conor Dougherty I cover housing. Amid the sprawl of Orange County, Calif., is something unusual: A 300,000-person city with a dense ...

Eric Gutiérrez, 6th February 2026. A Python implementation of a 1-hidden layer neural network built entirely from first principles. This project avoids deep learning libraries (like TensorFlow or ...

Deep Learning Crash Course: A Hands-On, Project-Based Introduction to Artificial Intelligence is written by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, ...