When an agent does something, the whole company should learn from it, so that every developer gets access to the shared ...

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...

Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances we generated "model ...

MATLAB is one of the most widely utilized computational software platforms. The initial version was developed in the late 1970s by Cleve Moler, the chairman of the computer science department at the ...

This demo shows how to use transformer networks to model the daily prices of stocks in MATLAB®. We will predict the price trends of three individual stocks and use the predicted time series values to ...

System identification learns models of dynamical systems from input–output measurements. Estimated models should generalize by predicting system’s output responses to new, previously unseen inputs.

a. I have been lucky to be in a position that I can have a positive impact on the career of hundreds (if not thousands) of people. Fortunately, I feel relatively secure in my career and I have ...

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of ...

Single particle tracking in live cell fluorescence microscopy imaging data serves as a powerful tool to study the dynamics of a wide range of different particles. Here, “particle” is a generic term ...

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of ...