Anthony Strock

This paper validates an explainable AI framework to identify brain regions affected by excitation/inhibition imbalance using simulations and empirical data from autism.

The paper develops personalized deep neural networks to model learning disabilities in children, revealing how neuronal hyperexcitability affects learning performance and neural representation structures.

The paper introduces a reservoir model of gated working memory using a random recurrent neural network, showing it can robustly store arbitrary information over time with minimal hypotheses.