Interpretability

Information integration is a measure, developed by Tononi and co-researchers, of the capacity for dynamic neural networks to be in informational states ... Sep 14, 2016 ... Feed forward neural networks (FF or FFNN) and perceptrons (P) are very straight forward, they feed information from the front to the back (input ... Aug 30, 2023 ... To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of 'Pong' to ... Mar 4, 2016 ... For the multiplex neural networks, we find that intra and inter couplings will complementarily cooperate to complex behaviour if the two neural ... Feb 22, 2023 ... We compare this control to a regularly-trained neural network, and show that perturbations on nodes carrying relay information cause failures in ... Sep 27, 2024 ... We exploit large-scale content consumption information from the perspective of heterogeneous networks and learn latent customer representation ... Jun 29, 2018 ... The brain has numerous levels of interaction ranging from gene networks that control cell function to neural circuits that control behavior. Apr 8, 2025 ... Neural networks are computational models that learn how to recognize patterns, develop predictions, and process data. By using algorithms, ... Apr 17, 2019 ... ... learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are ... same ink serve more than one informational purpose. Using large ... Physics-Informed Neural Networks for Anomaly Detection: A Practitioner's Guide ...