This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

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 ...

AI projects are not for the faint-hearted – they need to be properly resourced with the different skills required: data ...

3. Timeliness and currency: Outdated information undermines AI performance. In fast-changing fields, models that rely on ...

While some AI courses focus purely on concepts, many beginner programs will touch on programming. Python is the go-to ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Cui, J.X., Liu, K.H. and Liang, X.J. (2026) A Brief Discussion on the Theory and Application of Artificial Intelligence in ...

There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...

EPFL researchers have developed new software—now spun-off into a start-up—that eliminates the need for data to be sent to third-party cloud services when AI is used to complete a task. This could ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

AI (Artificial Intelligence) is a broad concept and its goal is to create intelligent systems whereas Machine Learning is a specific approach to reach the same goal.