A biologically grounded computational model built to mimic real neural circuits, not trained on animal data, learned a visual categorization task just as actual lab animals do, matching their accuracy ...

A new computational model of the brain based closely on its biology and physiology has not only learned a simple visual ...

In the brain, synaptic plasticity - the ability to change neuronal connections over time - is fundamental to learning and memory. Traditionally, science has focused on nerve cells and their synapses.

The deep learning field has been dominated by “large models” requiring massive computational resources and energy, leading to unsustainable environmental and economic challenges. To address this, ...

The brain’s chatter has always been partly out of reach, with electrical spikes easy to record but the chemical whispers ...

USC researchers built artificial neurons that replicate real brain processes using ion-based diffusive memristors. These devices emulate how neurons use chemicals to transmit and process signals, ...

Memories and learning processes are based on changes in the brain's neuronal connections, and as a result, in signal ...

An interactive toolbox for standardizing, validating, simulating, reducing, and exploring detailed biophysical models that can be used to reveal how morpho-electric properties map to dendritic and ...

Scientists have developed an AI-driven approach that significantly advances our understanding of the hand's complex motor functions. The team used a creative machine learning strategy that combined ...